{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实验1，调整gamma，观察\n",
    "\n",
    "### 环境及智能体\n",
    "\n",
    "- 环境： MinorityGame_1， 少数派游戏，101人博弈，无穷博弈，创建环境时可自定义环境承载量，本实验中固定为50\n",
    "- 智能体：QLearningAgent，创建智能体时支持自定义学习率、折扣因子以及探索系数，本实验中固定学习率和探索系数，观察不同折扣因子对玩家决策及收益的影响\n",
    "\n",
    "### 实验结果\n",
    "\n",
    "101人少数博弈，固定环境承载量为50，学习率为0.1，探索系数为0.1时，选取不同的折扣因子,观察玩家决策和平均收益未发现明显影响。\n",
    "\n",
    "### 实验内容"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 实验准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MG环境\n",
    "import gym\n",
    "from gym import spaces\n",
    "from gym.utils import seeding\n",
    "import random\n",
    "import numpy as np\n",
    "import copy\n",
    "import math, random\n",
    "from collections import deque\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# %matplotlib inline\n",
    "\n",
    "class MinorityGame_1(gym.Env):\n",
    "    '''\n",
    "    Minority Game, we have some agent, every agent can choose 1 or 0 every day.\n",
    "    In midnight, all of the day to make a few choices of the agent to get +1 reward.\n",
    "    '''\n",
    "\n",
    "    def __init__(self, env_max=50):\n",
    "        '''\n",
    "        环境初始化:\n",
    "        玩家数固定101;\n",
    "        env_max 环境承载量，选择1能获取收益的最大人数，默认为50;\n",
    "        action_space 动作空间，大小为2，玩家只能选择0或1;\n",
    "        observation_space 观测空间，这个环境使用2，玩家立足于上一次博弈的状态;\n",
    "        '''\n",
    "        self.env_max = env_max\n",
    "        self.action_space = spaces.Discrete(2)\n",
    "        self.observation_space = spaces.Discrete(2)\n",
    "        self.seed()\n",
    "\n",
    "\n",
    "    def seed(self, seed=None):\n",
    "        '''\n",
    "        设置seed\n",
    "        '''\n",
    "        self.np_random, seed = seeding.np_random(seed)\n",
    "        return [seed]\n",
    "\n",
    "    def step(self, action_101):\n",
    "        '''\n",
    "        每一步博弈：\n",
    "        1. 检查输入是否合法\n",
    "        2. 统计选择1的人数allpick，allpick不超过env_max则1获胜，否则0获胜\n",
    "        3. 返回S(玩家本回合动作), R(所有玩家的奖励列表), done(False，无尽博弈)\n",
    "        '''\n",
    "        assert len(action_101) == 101\n",
    "        assert all(map(lambda x:self.action_space.contains(x), action_101))\n",
    "        allpick = sum(action_101)\n",
    "        reward_101 = []\n",
    "        for action in action_101:\n",
    "            if action == 1 and allpick <= self.env_max or action == 0 and allpick > self.env_max:\n",
    "                reward_101.append(1)\n",
    "            else:\n",
    "                reward_101.append(0)\n",
    "\n",
    "        done = True\n",
    "\n",
    "        return action_101, reward_101, done, {}\n",
    "\n",
    "    def reset(self):\n",
    "        '''\n",
    "        重置环境，每轮第一次博弈给所有玩家一个随机状态\n",
    "        '''\n",
    "        # return [0]*101\n",
    "        return [random.randint(0,1) for _ in range(101)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Qlearning智能体\n",
    "class QLearningAgent:\n",
    "    '''\n",
    "    Q-learning智能体实现\n",
    "    '''\n",
    "\n",
    "    def __init__(self, env, gamma=0.9, learning_rate=0.1, epsilon=0.1):\n",
    "        '''\n",
    "        Q-learning智能体初始化:\n",
    "        env 智能体的博弈环境；\n",
    "        gamma 折扣因子，n步后的奖励为 pow(gamma, n)*Rn, gamma越大表示越重视长期收益。\n",
    "        learning_rata 学习率，Qlearning 更新过程为:Q(s,a) += learning_rate * (R + gamma * Qmax - Q(s,a)),\n",
    "                      学习率越大表示越不依赖过去学习的结果\n",
    "        '''\n",
    "        self.gamma = gamma\n",
    "        self.learning_rate = learning_rate\n",
    "        self.epsilon = epsilon\n",
    "        self.action_n = env.action_space.n\n",
    "        self.q = np.zeros((env.observation_space.n, env.action_space.n))\n",
    "        \n",
    "\n",
    "    def decide(self, state):\n",
    "        '''\n",
    "        epsilon-greedy策略，另外Q表所有值相等表示智能体还没有学到任何经验，这时也鼓励探索。\n",
    "        '''\n",
    "        if np.random.uniform() > self.epsilon and self.q[state].argmax() != self.q[state].argmin():\n",
    "            action = self.q[state].argmax()\n",
    "        else:\n",
    "            action = 0 if np.random.randint(self.action_n) < 0.5 else 1\n",
    "        return action\n",
    "    \n",
    "    def learn(self, state, action, reward, next_state, done):\n",
    "        '''\n",
    "        Q(s,a) += learning_rate * (R + gamma * Qmax - Q(s,a)\n",
    "        '''\n",
    "        u = reward + self.gamma * self.q[next_state].max()\n",
    "        td_error = u - self.q[state, action]\n",
    "        self.q[state, action] += self.learning_rate * td_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "def play_qlearning(env, agent_101, episodes,render=False):\n",
    "    '''\n",
    "    Qlearning智能体一次游戏\n",
    "    参数:\n",
    "    env: 游戏环境\n",
    "    agent_101：101个智能体列表\n",
    "    episodes: 最大轮数\n",
    "    render：是否图形显示\n",
    "    返回值：\n",
    "    episode_reward\n",
    "    '''\n",
    "    episode_rewards = []\n",
    "    episode_actions = []\n",
    "    # 初始化S\n",
    "    observation_101 = env.reset()\n",
    "    for _ in range(episodes):\n",
    "        # 各智能体根据环境选择动作\n",
    "        action_101 = [agent.decide(observation) for agent, observation in zip(agent_101, observation_101)]\n",
    "        # 执行动作后得到环境奖励和新状态\n",
    "        next_observation_101, reward_101, done, _ = env.step(action_101)\n",
    "        # 为所有智能体更新Q表\n",
    "        for agent, observation, action, reward, next_observation in zip(agent_101, observation_101, action_101, reward_101, next_observation_101):\n",
    "            agent.learn(observation, action, reward, next_observation,done)\n",
    "        # 更新状态\n",
    "        observation = next_observation\n",
    "        # 上面是Q-learning完整的一步，下面是数据统计\n",
    "        # 统计动作\n",
    "        episode_actions.append(action_101)\n",
    "        # 统计奖励\n",
    "        episode_rewards.append(reward_101)\n",
    "    return episode_rewards, episode_actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "def moving_average(lst, N):\n",
    "    '''\n",
    "    计算移动平均\n",
    "    参数:\n",
    "    lst: 输入列表\n",
    "    N: 窗口大小\n",
    "    返回值:\n",
    "    res: 移动平均列表\n",
    "    '''\n",
    "    res = []\n",
    "    for i in range(len(lst)):\n",
    "        l = max(i-N+1, 0)\n",
    "        r = i+1\n",
    "        res.append(sum(lst[l:r])/(r-l))\n",
    "    return res\n",
    "\n",
    "def density(lst):\n",
    "    '''\n",
    "    将玩家决策原始数据转换成密度数据\n",
    "    参数:\n",
    "    lst: 玩家决策原始数据\n",
    "    返回值:\n",
    "    res: 玩家决策密度数据\n",
    "    例:\n",
    "    输入: [1,1,2,3]\n",
    "    输出: [0,2,1,1] + [0]*98\n",
    "    '''\n",
    "    from collections import Counter\n",
    "    res = [0] * 102\n",
    "    tbl = Counter(lst)\n",
    "    for i in tbl:\n",
    "        res[i] = tbl[i]/len(lst)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实验过程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 基础测试：gamma = 0.1, learning_rate=0.1, epislon=0,1, 博弈3000次，观察玩家收益和动作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建环境\n",
    "env = MinorityGame_1(50)\n",
    "# 创建玩家\n",
    "agent_101 = [QLearningAgent(env,gamma=0.1,learning_rate=0.1,epsilon=0.1) for _ in range(101)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rewards_0, actions_0 = play_qlearning(env,agent_101,3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 玩家总收益（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot(moving_average([sum(reward) for reward in rewards_0],1))\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 选择1的人数（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot([sum(action) for action in actions_0])\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 玩家总收益10轮移动平均（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot(moving_average([sum(reward) for reward in rewards_0],10))\n",
    "plt.plot([50]*3000)\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 调整gamma，观测对结果的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "玩家总收益\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "选择1的人数\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "玩家总收益10轮移动平均\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建环境\n",
    "env = MinorityGame_1(50)\n",
    "# 创建玩家\n",
    "agent_101 = [QLearningAgent(env,gamma=0.05,learning_rate=0.1,epsilon=0.1) for _ in range(101)]\n",
    "# 博弈\n",
    "rewards_1a, actions_1a = play_qlearning(env,agent_101,3000)\n",
    "print(\"玩家总收益\")\n",
    "# 玩家总收益（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot(moving_average([sum(reward) for reward in rewards_1a],1))\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)\n",
    "print(\"选择1的人数\")\n",
    "# 选择1的人数（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot([sum(action) for action in actions_1a])\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)\n",
    "print(\"玩家总收益10轮移动平均\")\n",
    "# 玩家总收益10轮移动平均（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot(moving_average([sum(reward) for reward in rewards_1a],10))\n",
    "plt.plot([50]*3000)\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "玩家总收益\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "选择1的人数\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "玩家总收益10轮移动平均\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建环境\n",
    "env = MinorityGame_1(50)\n",
    "# 创建玩家\n",
    "agent_101 = [QLearningAgent(env,gamma=0.3,learning_rate=0.1,epsilon=0.1) for _ in range(101)]\n",
    "# 博弈\n",
    "rewards_1b, actions_1b = play_qlearning(env,agent_101,3000)\n",
    "print(\"玩家总收益\")\n",
    "# 玩家总收益（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot(moving_average([sum(reward) for reward in rewards_1b],1))\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)\n",
    "print(\"选择1的人数\")\n",
    "# 选择1的人数（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot([sum(action) for action in actions_1b])\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)\n",
    "print(\"玩家总收益10轮移动平均\")\n",
    "# 玩家总收益10轮移动平均（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot(moving_average([sum(reward) for reward in rewards_1b],10))\n",
    "plt.plot([50]*3000)\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "玩家总收益\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "选择1的人数\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "玩家总收益10轮移动平均\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建环境\n",
    "env = MinorityGame_1(50)\n",
    "# 创建玩家\n",
    "agent_101 = [QLearningAgent(env,gamma=0.6,learning_rate=0.1,epsilon=0.1) for _ in range(101)]\n",
    "# 博弈\n",
    "rewards_1c, actions_1c = play_qlearning(env,agent_101,3000)\n",
    "print(\"玩家总收益\")\n",
    "# 玩家总收益（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot(moving_average([sum(reward) for reward in rewards_1c],1))\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)\n",
    "print(\"选择1的人数\")\n",
    "# 选择1的人数（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot([sum(action) for action in actions_1c])\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)\n",
    "print(\"玩家总收益10轮移动平均\")\n",
    "# 玩家总收益10轮移动平均（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot(moving_average([sum(reward) for reward in rewards_1c],10))\n",
    "plt.plot([50]*3000)\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "玩家总收益\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "选择1的人数\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "玩家总收益10轮移动平均\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建环境\n",
    "env = MinorityGame_1(50)\n",
    "# 创建玩家\n",
    "agent_101 = [QLearningAgent(env,gamma=0.9,learning_rate=0.1,epsilon=0.1) for _ in range(101)]\n",
    "# 博弈\n",
    "rewards_1d, actions_1d = play_qlearning(env,agent_101,3000)\n",
    "print(\"玩家总收益\")\n",
    "# 玩家总收益（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot(moving_average([sum(reward) for reward in rewards_1d],1))\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)\n",
    "print(\"选择1的人数\")\n",
    "# 选择1的人数（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot([sum(action) for action in actions_1d])\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)\n",
    "print(\"玩家总收益10轮移动平均\")\n",
    "# 玩家总收益10轮移动平均（仅绘制前500轮）\n",
    "plt.clf()\n",
    "plt.plot(moving_average([sum(reward) for reward in rewards_1d],10))\n",
    "plt.plot([50]*3000)\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.pause(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不同gamma对平均收益的影响\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 玩家总收益10轮移动平均，不同gamma对比\n",
    "print(\"不同gamma对平均收益的影响\")\n",
    "plt.clf()\n",
    "plt.xlabel(\"Round\")\n",
    "plt.ylabel(\"Reward\")\n",
    "plt.plot(moving_average([sum(reward) for reward in rewards_1d],10),label ='0.9')\n",
    "plt.plot(moving_average([sum(reward) for reward in rewards_1c],10),label ='0.6')\n",
    "plt.plot(moving_average([sum(reward) for reward in rewards_1b],10),label ='0.3')\n",
    "# plt.plot(moving_average([sum(reward) for reward in rewards_0],10),label ='0.1')\n",
    "plt.plot(moving_average([sum(reward) for reward in rewards_1a],10),label ='0.05')\n",
    "plt.plot([50]*3000)\n",
    "plt.ylim(35,65)\n",
    "plt.xlim(0,500)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不同gamma对玩家决策的影响\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 玩家决策，不同gamma对比\n",
    "print(\"不同gamma对玩家决策的影响\")\n",
    "\n",
    "plt.clf()\n",
    "plt.plot(moving_average([sum(reward) for reward in actions_1d],1),label ='0.9')\n",
    "plt.plot(moving_average([sum(reward) for reward in actions_1c],1),label ='0.6')\n",
    "plt.plot(moving_average([sum(reward) for reward in actions_1b],1),label ='0.3')\n",
    "plt.plot(moving_average([sum(reward) for reward in actions_0],1),label ='0.1')\n",
    "plt.plot(moving_average([sum(reward) for reward in actions_1a],1),label ='0.05')\n",
    "plt.plot([50]*3000)\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,3000)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不同gamma对玩家决策的影响\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 玩家决策，不同gamma对比\n",
    "print(\"不同gamma对玩家决策的影响\")\n",
    "\n",
    "plt.clf()\n",
    "plt.plot(moving_average([sum(reward) for reward in actions_1d],1),label ='0.9', alpha = .7)\n",
    "plt.plot(moving_average([sum(reward) for reward in actions_1c],1),label ='0.6', alpha = .7)\n",
    "plt.plot(moving_average([sum(reward) for reward in actions_1b],1),label ='0.3', alpha = .7)\n",
    "plt.plot(moving_average([sum(reward) for reward in actions_0],1),label ='0.1', alpha = .7)\n",
    "plt.plot(moving_average([sum(reward) for reward in actions_1a],1),label ='0.05', alpha = .7)\n",
    "plt.plot([50]*3000)\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,3000)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不同gamma对玩家决策的影响\n"
     ]
    },
    {
     "data": {
      "image/png": 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0qwewv3FtqaD6XsMbybiw0tg4/tJDKFE30TWwWHd/jDTn+7ZWHXh59kSMktu1LBOWbWD4zR0PO/3xrwD46bQihIiptRmFhvKvL4b+07t8WbvVDoB75fOwaxNc8e6+yX8AkaE9NOanTzD+Trn6wAtzgHhm7TOMyR3DzL4z47ZLXafmLw8g7HaGrV1ziKTbDUEPpOQeaimifPw7WPUyFE6CEWd1/Txx8C3AYdUDSIinvtNdIU3ntWVl6HqMd1a/FUo+OQiCdQFP7W53ry5voqax2fjRcmANgHvRIoIVe/Cuw4G9KtMXahezbwtn1RUDUOut5ZMde34XDovRAwhsd6G5D10Y0Bvy8szaZ/i8/PMO+6Sva3JVtFawqHyR8UPXYcULLKn4im1N2wwDu/IlCB28yQ6N8+bR8PLLXU617g51TMgoA0a92KMR7Aa61Hmz+E2aF39FYNu2Dvs9S5YSKC5OfHLwMMsg4DXH88QenL926Bz8sYzDqgeQkLotkHJ0wl3PfrWdv3y4CYHg/Ml9jI1/n2D8vbv5IAm4G1qrIK1np7vPenIx99vKucgGeBKHW/YX5ddehyU1laHLl3V+UHjvlK87ECbZEVOF2gxIgxEeuu6T69jSuIWllywlyZbUaTkOq4N+1ZLUL1LZVdNI7+v3Soz9xtJdS3ls5WMArL1ibdw+3d81pf3smmf4oPRDllyyBLbMh/d+zc/69zXKnPQnePeX0FgKs36/X2VPhNbczK4/3AVA6ozjcRT23uM5nlBHZdpmAA4kH5V+xF1f38Xw+40Q1PB24yxlV1yRcDtw+IWA2tqRZe/U6/r6DRQcAHF2x2HbA/Bqhle4u3hyg8f4fkC9J8F3BGI8npKa1sixe0X9VmjdtffnmQSL1xFa/uFuPWunML0qf7zBCmkhVtWsIqzpLCvt+gyhzQ2baQ3GJ9VrW1auu/eQbtv0TLvkLEpJ88JP8X5nTF8NbN9OqKHF2NdqhO8qmncAUOWuYmN95wOnDquDdK9x0bBPN+Qo79xQyWCQsi++pbxh/zb8WOXX3mOO6wFUr496eSbNFgvFdjvbmrfiDXuNWLpo17zc1XzndKB7d/M+y5aA1vlUwNfWfIUnRiGvKm8iENbwh/2srW1vtKLHaY3GNX3r1qPvpjezsnolnpCHjfUbKW0upaylDD3mesHS0s5lT0BxYzGfFW/fYw8kkeHpMu4aqFpt/N+wHVoqu3xqaOdOQjs79oxDVVUEtm83BmV3riRUtp1g6XZCK+cTLCvbfaFt7X0vHSpvOHF9LmksocHfrs74GinbuBtnrosctj2AxlAqydYGaNrR6TEWM2amJ6pcQTc40wA48ZEv6Jnu4tv/t5eTk/alNxEzlbHqr8+i15XT/7ZT4KwnOxyalWzHGTINUzsDsGDHAm778jZOSH2Ed5YFWXjzDAb2SN3tpaWUnPfeeYzJHcPLp78c3d5VDy5sGoAuzMYcW1dC+Pb/YwcwbM1qtp16GhanYOg5GEazfiuOkBev1crPPr6KWl8dyy5Zhsvm6lCWw+JAN+2gxWGFeZdB8cdw63ZIzu5w/M5bb8Mzfz6XzP49Kx+7uGv31gViG6An5CHVEX3esT0A+Y9jEK50uKM8su0nvfIocTjIat4eKStdanGd+q9bSri2oCe3BCsjC2XiqFgOz50MM26HmXd02L1852bu/e7nzN9xBs+feT/lDV7OfnIxF0/tiyP/Dd4ofoOPz/2YXqm9DDlDUadHa2xEc7spPe88Uk84gT7/6FgfAT4s/ZCWYAuLKxdHtq04/n+R/7eecmpiL7wTfvTuj9CD2dzvncucsZ37tw6rY9+nc756kfH3dzXw+Djj/y622ZJZJwIdexYlM0+I/D/gtGq2fRA/q223zyBkKv7g3jkoie5eSsk5755DQUoBH533UXTHc6fSt7b7s9EO2x6Aq82Kmg9xc23Uqod3bkP6PFjMMZOaFj9ba9t5t+4a8DVS02I03F0t0QastbbGeTVxJPD4pZSEgwGaao04fas/hC8YryVlOEy4oYGaph2E/MZj1Vrc+BscBL+LxsF1qUeseZLdip0gdRaLYQCkREpJrbeWOp8xK2jhJmOhdXVzxxCE7vcTbm2lptWPlBL3LkMhbaxaTWN1dOzE1xr1rlqCLZH/A03lNAdiGkqbAdCjg1F6065ohTYR1laOqVwX+e03Y7N6wKzC7hpw1+DUJGleSa15L62+JsKNMdNd3bW4/SE0XZJkvg6Lw2Yof+g4NdZdC7pG6/z5xvMz64iUMvK8ABprKwmHutjjC/kiq7B9MffZFDC2hWqM0Fys1yw1AYEWaPuCnZSUOIweq95onOcJeiDoxR8zsFdhvvft0hc35tPqD1HeXI00Q2fUbIjsq/PVRbznnc3Gu9raYgzENtU10UvuYNWOOjY1bMIZlGzeuQpd6uCuRfqiCkhrbESaRszz9deEGxs7XXQUq/wBdHdiZaoHAmitRm9Ta2pCBuOfueb3k+yXWBwNiXtrrdWA0aOTLW6cnQwxhKrajY+1VscZC6lDOGCBoActJKiV1rgeR52vzngmgOb2oHsTK2ap64TrO4456uGOarKm0WPIEKsvpITWaprCfkJAk68Ot7eWluYEPYbWaqMX2VaHQn5CvnY9dynZ0mhEQCo9ldHzAPaD8ofD2QC0DTiFvNz72Suc98Fs5q39EkJ+imedTsVFsyM9gBe+2cGsvy7iv99sjhbw/GnwQBFT7lvYoewtk6ew4+JLOl60djP8dSgs+Vfc5v8ur+Dbf15D5pPD0IJ+Rt/9MTMf/jzumOoHHqT4mGm8+oszKHm7J1pIoPkMBdVSHFXAH5V+xOzXZ+MOuvGGNDam+JjdpzeN6BDy8t629zj5jZPZ3mIogxuT/w+IN2BtbDtzDsWTp3DUfQvZ9PLrVMyczaCdkt++obNrxrGRRnD9819Hzpk2d5rxz4Z3+Nlrszj21WOjBZoKUNeiSku7fzjMvTDye3XtalKH/JnxDWvRMo1BX/dn8c8CJDu3rePCT+DZxzSsmiFH430PUnz0MejBIDTugIcH8cS9v+br7btIM/WrEDFKxB+THsNdCw8PQv/oz5FNLi1EWNN5bt1zzJw3k0p3Jf6WeuxPjOfDFx7o8LwS8vQseKAfEN8DaAo04V68mJLjZtD66WcR5QmgBc3ns3OFKaehIEfu0Hn6cY2JxboR0gi68cQYgJBpYO3+FnhkGCx7FoCx973EaW+fyH+rvzEONKfFbmvexsx5M3lt82sAtPoNp8OvGQ/LNmcW//jg7wy2PE6KPYXnH9XIOecm/rXicXhsDHLjh1GZm5oiISHp91N89DFU33d/lwaH5atXJtxeeuFFbJk8BYAtRx1NxY2/idu/4yc/4d+PGjLbLO1muJR8An8dAsULqPjVrxly0Z9ISmCzfevXx3njsmqtcd7KFyPbdq3IoPitnkhvM1ve6MX6j/J5ZdMrgKH8T379ZBaWGXpgy6RJbDk28Qy1+qefoXjasYR2tXMCEzyis37/OjWLXzL0RYWZzmzNa8i/DmF6qpff98hhevEzHP3fE5j29unxvZt1bxj38GB/+PJhY9vzpxKoilfqq3cu57z3zouKUfKpcd6Wj9hfHLYGQGgSqYPu8/BtpdHQvi5fib7J8Kbdm+rxx85CkZJvV6+P/nYbL9FJYk/Qv359x41t1vzLvxozOEwWb61jQMMXAARrS4COCrnl/fcBOGmV8aL1oAU9YHhYnopo97a0pRS/5qc52Iw3EKbZEiRoEey02cDfzIfbPySshSgxPeztdhtIPXq9mIoUKjc8fl1C6RdGT2F4hWTcduOYljVGGSVl8QPMUkrY8C6rXM74+4+EgGIMQMAC2z6PjA+0xfIzAl7co401gK0LjcZlsQPJOQA07ljHuM2GHJlm5yz8vvHupNcLTYZXNNOyEk0PkuYzjhXBmFhnbFjMHAvSNnwa2eTUgpQ3NvN2ydsA1Ppqca99n1Thp3bntsi96l4v/rA/4gXGUbO+7aHgi4nZNvobI8+38bVX0X3R962HzGbTXGH89Rpe43DT0RtUKfGEPRDy4rVEm1hLsAWkxOY2ZofJJYZxtziM3svnjYbnL4WdgKeV1VVGjHdZ1VIAGszvZYT0aO/VFQLdup0Uewo2HWw6fLZjAYS8yIaKyHHhxkZku3Trze++G533vxtDoNfHh2F1Xccb8hLYaNSFtt60+9NP444LrFod+d9mtRj1zpyxo22eT0AAtZtwLzJmTSUl6JQH2q090HeYZZZEV2Q3bzcWgYVrjJ5CfhN8Uvqx4UE3bCGkhyhr3gEBw8OWXi+hBHP02+Roe++Ra4Y7Ts/s467BV2nUHblmnnFvFcvwmgb//dSU+BPKl0bCw3JzjALfudIYM6hcSViPV8eVjfHPvX6LoWOoXsf+4rAYA5AJpr/u+KQHALplMbMvDfOv3kkMKfk7WtMpkWM8QaPy9mmt5pFFf+eLsZPZuKKA3tMaqFqaSdFJddyV/CJprm38vz46q2sLGZM7JqEMz697nkdWPMJKwO7eBUE3Je/lkZIfwDocaren07rBxvn9LuSFzzTKMtOore7DCfMv4Tj38VzpDxO7FtEXtiDDOlIIWmuceF79Ka7TH+f4Cx5n/ekWvGf6uWHJy5xQEaDsHAvVWVb6u3expGoJ18zXOXHVOv55moVU3cGH828lJTWT8LQvsP05lxVF1zKiPt4L2OyBIiDdK6nIgcJ6qLzgfLY5wDktfkWyL+wjubki8vaH/u1KHFmGspnrcLAoNYU2n2uz7mQsYfjbaLhlCyMve5AbCjVSApJl+gqOTRERRVCXBkOzB4K3HlfzNpqckOKH7FaozwDMkMMjf7yZ36a/DkDKR27uyfRSZc4glaZR3/5xLvqKv5D/xhSOnns0x7WO50mgWaQDRjc4yb6d5RdP43Lgj5dYaQ22YtnyAQAjvtrIZ0cNZ/PAQcxYUcIzJ1toPuNo7pmfgXfZMjb2t5G3ropNfXozrFxyddovOH1idMbWy8s3cF/QmLlUtfQLPh2hMdXcd583nws/lPQfugbXmB8b96vrOEOGEj33a0ngj4/DeUNxx3i+458o5eYsnYZZHho2p1D9aivPWs4iqY9hrHxmuHPZ49+QducU5l5iZWaj5Gf3f4i2/E+MXHEbZIJGvCJP91m49BdRhXj3ndsIn21BeqLhvhc/WkGfvFHE1n6pafg3vMP172mMKJNcf71RIX77ukZRtfF77Fadsk/j59hvHjGSG66x8pj5W2tIMKhdsSLmQpIxf/4NdWPS6JH0Lp8WXMMDfEJlUV/e+OwewGjrqTGRRvn7TMSMG+Htp4DoRwW1hkasQKgpSMkrPXjgPAu3Y9SZwN/OBgwH5PTnN/PBYyfSetwm5r2YzuqznoOkm8GcZzPtlan8YuC/I+/0orfO45L69QwEtP87A4iOPSUyAL3dtWCxowPjaj7kuu/6MfPmD3CNSTMaYjtCz52MvWg68tK32XTnl0ABwiIZds0W+PtEAMJ6/HXW7jR6FoW1kkee0bju0o/Y3L8vbPs3a4HL0noBLXSHw7YH0IZFF2S2GIojKINom6LxSY85Z7xPaw2pYT+nrfgSgKZtyeghC746O+dbP2dnihFL/qj0o7iufCyPf/c4ACtNrzi4aTkhj42mbSmk4sVfb0d32+jRBHnNMGlHK1s2GXnuyi3fEAzHjwnUh23IsKAuXWCV0LR9HYESo/dw6nKd5oCXMXXG74J6qLVa+arya0J6iOFlhiKZvk5y6ifGK+pRW8zOXYaHM7H0/7C1lkYvJiXuZsPNzm0GLeatuoLgIn58pMmzK+q9QkT5A3yenMT8pOjg50qRAQUTjGmq3npsrT6mbZRYJRQ7mrA6om6bxwJkDwAgzVOKz2lU6Gy36d2b6zWuIzqoaG3WGbUjHAkBtfU+/A0OgsXlbG3eCsAqp+FtrY6J7WdqNYwqk4wyn1ejp4b0CsOLyyhvpmcT5O8ynvFx63RW16ym5YMPCNfWMnhpFRlemLpZkuEFcr+iormJZJvhuS3csh2tyQhBOcKwouybyHUvXGRcz7vGDDm27sIlZVwM27ngawh68NgckW0ZtWGmbpHYdEn9RuMZ15ryAfhDPqRdJ3+dAAAgAElEQVSEtGrjHns2SmasNXotnoUf4PIb70yK+F6t8HT03lt3utC9UeWQGW7g7SXt5tdrGoHVLzNjnaRHC2S1GuVMLjZ+Axy3PnHPYGRZTE80wSwa1r8Z+TcpCCmlW2j+Yi1SQqB8JZVOo72U26M+aGbMfWghAV89GheOBNBqjG6Wb6dRp09YLSMOpLc2+qx77XAzsHwnDR6jPQ9Y1IS0RCcg+PQQc795L/J7e/UmGhxGQUF3/Pz9RAYgNejDFmikxWJBCnhxqZFCxb8m8USN5r5TYddafN9Fkz5KXSAby6DZ6HGIdtdZuMUI4d2w2bjXWaujPVgdsDTu3TqDRBwePYA97J/vNBplqc/J9nlRBX7cC9ey8IIsUssnJzwv0GInU/jwWeCklTrjF37KR78qjBrot66DggmctGUVYT3Mz+ZrVDjTmFoUwP1xNH76ie9Ohgsf44C+dVFpw83VjN6uc+5iD/Z267ibAjZcCGrToUczeDc10fKiMe4QtkLzpb+hwG+0sqSgpMZmZU31cnJsmeQ3GSGBEWUyYqGlJvjD549QlJvNPXUNrHA5yTD3/Vq8ybSQMTBY0CBJadeVni2WxP1+6O4zOX5bCM6NDwGdsEpn2qIkGBytaBavBWbeCS+fy5ffLCbWF/S6QIu2ORxhuCiwiVcsNvKDZXichrd181s6WwqiZdq1MB4huDYznzvNbRlm428pS8afEu1Lbf7z77l3TRiL0NCOEmwMN0TmSp9XEW1MFyzSCLx8Jxv8KVhmabQ1jQFmVG9IJfzk3d1MNZSCsAyQbs/GE/IhrF6als4FjDBLW3iiNA+KzIja67WrGHfcSMZOqiI0uhcpgXY1efHfqE3KJsMjue/fUQche5cFd5aOq8rK1GKdSVsk9WmCHaOaKXkv+v0kRxhK8wUjyiXFbz/Hdccb+66er1FV83DkOF+C8I2nykmqayGQQ8AGI0LbqAvFhz1lMEjtM9GUHpOKdU5Zo9OWk+DR/wtTlZ14deo186Pvc/WbL2EmNGH7CRPRQnb6Hb0ZMHpUeU0gpCTUGOBhXx6nflrFLQXw8LlW0j+JKszMmNejhwS4zBBkDDv+Nh+bNY+M0dGwpjQl3t6YRDoQskJ6q4bUBSk7jFXmTo9kfWgAVsxxJSkZF5wbKSPFDyFTGwaa7XHXfDI1k3Pa3X926ld4tvem9f1c/pQaZuWgqJy96iU3vqNh1cDvAIsOqwPlPHlhMre+9Wyk3QJseTMfURDkV5fmcuny+PppCWkk6ZIJup96khhUKXnyyTABO5T2SyOry5/a6pzDwgDsKQdGm2c1cJWdWHMxcmeAfF8taXp8Hp2w12j+wRbj9nbY7Vz7kQ7s4NVtiyIGQK6eS/W6eezqayyQOfk7CTiQ/cC/cVOkPJu7jhSPUeFH7Ihe39tcxWWf6qZCiG+EHr8NF1CbIaBcEl4arVQ9GyHdF/WakoJQY7Wy01fDZK0Qm17Hy8dbyG/RGbATBlQbDWKlvpCVaancU9fAZ8nJkW9tXsnbtIYycOMiw2N4/bFMEt8B0YZmNF4r+Y2S6qzowz9lhY7La2FCSfReLG4gdzAAHyz8nMtj79EJFckW2gInjhCsCzbgS+1JcksF/ph2NCRmarauCTY7HPjddjC77wNjJnoEY8bCRs9vW/0pcVe6cMdoiZzGEG2d2HO/bpPZzsuBHlyC8b4cGnw6NIcTNtczurRzV8OqGQYA6UJqSTjsPioa6+hpNpE273TxCAtFNUbZ+duspNXoVK9OxzPWQq+WEO0rc4sQTCiJetQAjmYLlbqdAUBuA4zeIQHJMz0shL3R889o8rBGM4xhaUUpbc31pO8kTd89GznO5u14X/5GO/Q3nk1DGqQEdX4eepVK2k2r3RX1FoZVQJ+q6PV7N0CWe8+DxH02vkMrRrjMX2mEsVrKowv/ChqiZYxbbMPqlkzZAkhJalm0kmTE6D8taAG0DgZA+sOEsOGvaALiFbWz3mj3Vh0s5iWHbTDOd4RgaUsBU0wDMNwbwq9JMF2FlAC4zEcRaI5Xi+6wDYgfP7LpATyfluFqtjG0GYbujO4/bp1O/+qOz2nsBkFL/YY4A6CHLLDDRaXmw9FuUpYjBFmajsPUf4UxE5QC9WlkT+v+yuHDwgDs6TZc5gMQMQf+7jIr976kcdt/NfrWfRN3vNtnww40NjjZtDKPbdOctCma1dsW0zanZY3TwU15uWbZ0cIDTTYayysj3nfvekm2GUUZFWMAyqrLcGYJimo63kHQbzzaWvNtSyERZl81vd36kJNWShZm21jjLGPcLiMeuq6foDIrzPseJ48+rRndUCm57FMdT6adtJJoA2vWbQTMhpLmA3u7efxZC6PK/+fvR3fe+rrG8sGC9X0F0zbKiGebFdMQJ32n86fHn6O0Vz6WxuXE4nFBTVKMATAr8J99dn66KgN7J9P6dtQnUVKcykUxayYcXVh7UPlNFgUjNdqUrMud2HMoqGzXG+vVxAdpguPWdV7TfvdamLKBW3l9jIV0m8bwbeX03BptHtnmDL2vhwsu+dz4P92cNORrcDB2m86g8vjrlqzL4PVxyVz/dfzN2b2CHLO8rJjonKPFSqyi6b3WQW7A8MjTfDB7uY6eIGj7k3c7TnQIeqyIoKHcGtKgV5mFbUuz6LgKI8qAXQmeTxfS07Ru7LjK+1uHiwHm/79527gntyteid0xL16p9mmNvtvHnNkMXC3I90hzhCCeXUFBKka4qu0cZ8j426b83a7oOwKoDkfv75q3ISkmhPKbtzTc5m14WuzEBlcyE4TYnEFoDITolUA2r0uQSKsNaQpjrU2c9DHFD45w/DmXfabTkqtTszYj4TnZe1jX2RUOyzGAUIqOb6SP9yebDd2s39kx02RLCuDDiYLktnVUziQaBxgLv+zmND27X1C4xcZv34g2wNiX+VZKKjU2G+mhdM6pj9YUX70Df62XZYONcvrURmey9ItJ7+MVraR0sthP+gzvpDbDKKNN+cdOKf5qhKAix3gJJ31iw22R5JYZPYOaTOgbChvTOgAtZCHFD2culexYkMusGF3colloDBvXa6/82zNzjTkbqcBBUhBOWyb53Wt6ZLs/peNMmfPmzmOpy0lJcvy8fK9TUJUcvaE2A3DsRwFCm1IYET+ZIkJteQqjdxC33+2CNUV71jaT10ePaXvP7Skqj29IOzIlHiek7iabw8gynVM/q2YcZfSTzfx2fvwMjEw3BG1Qmyl462jjuj3NaILus3Lidx0bfGhdCn/6jxY5rg1nq8UYdyC+Tg+uMMrYMEZDs0jCPitWc2AwpxWuWqBz9Udd+1i4QER6wA1pZjsK7P759k6Qdit5H7NArE1gaj4bE3/98dvin1leTG9j/FIrozZa6FEZVcUPnWvBXWQ0eHdwz77r8sHx12sQ0V537zJL3LPv2QT9TAfIasbid+Yb8mS0ixzWpRtOaXInbT8tQY8MIK9VktxJjyrbTYcewKgdkmNWWMzeUEeyWhNu3itEV5NEHUj65/ST954UXfkYyNSRLp1Sm51hFZKgFVpSBBkeGVFwG/qaDaMF8pskusVKONuCo273CasqswQFjaYS7GNDC2kMaBE4rUGCrUalsrokml+wK1PQo1niSRKkeyVRX8OgKkuQ3SpxJlhPE3QIHEFJWQ9Bn1qJACwOnV1JVvKaJWELbCkUFFXLSCOrzhSkh3WS3IINfQQpUiJ06FsJ7jSJplnISFC5tFQdq9vSQb7dsSG7P8ODWxHu+MrVmkRkQLaNsEVQnQkZXkj1x6TY6CVI90Jec3Tbhj6CPnVEpnU2JwvSvDLO00gkZ2uyABk9rz1Bm3GOPQy6iHp5sf93RmmeICkoyG/as/KsKpAkuwUZ7SZX+O2Gcd1caEg+rBwssYuRAOmQWDoxSrGE7GAPGfcU2+glELTZ2FqgMaxc7vG+9oR0SERQ0JQGmftBWXSXkl6CQVUd21EbQQfYg+BzEHHsYtnQR9AvGCal2tql916VJejVGLMgLM1Gbuvuv7gVtkR8LirzIb8GrO2u43WAbhFxbSEWzWLUS69TJGyviajJEHHtqCv47fCz//x8RexndfeWw7MHIOz4k/NpmxXl0CC3xVD+mjCUSuRY00EQUuKKeVMJFu8B4IrxkAO6RrYbhFcn5Il6FJrfnInigKAdks0X7W03bb5Xo6H8E702i7n4SRfRWTnSapQRskJzipnGImahUH6TxOmzIKyG8u8dCuMwS09t7bwyWU0l7onpiWt7eLPJyQ6EtWMzDNqi25rTjb9hq6R3g+xQ4dsqeiyinYg61g6tPfanZjEalCdF7DYWWJkt8NvNZxZz3ZAdLLbEJ5bnCnwO4z1qdJwx0ZTS8f4tOhHlH7BBXXpbeCFa1yzSqIcQFVnQuRyxaBYioTGPM/76AvDb9eiPfaQp25TDDIl01spD3Z9EEsHj3H0ot9UlCNqNttuUmvjmHEEIWy1mCCWKZgHNLkBAyMyv1F75t7X3uPrY7n242mWv9dkNYxNLS7LAZwfNBn5bR+UPIIUFe7jzu7XqhsxdUeeNacZLiFX+bqeVusw9V4DOVk7vDYfFGIDDX8OEVY9Ffv/73L58N85KlbecS9+Lt9iPnm3hm+HRGj25VGfOQqPR1F5vIXOhjj0MyweJuMHMNqxODS1gPPRdNsHEjTJu9sGuTKM76HVJHpxt4xf/05lmToV75yjB9FUdyyzpCYPaLR7UhFF5XvuplaOWaxQ0QMmkIK9OjP9y0dzVHqw74i1L0oQJXDxgG9Rs4PYeOZywwBkZB+mML0cITs5sxP+NMR9jZ7YxiNcZpZe9wawvfkXjovhG8coMCyetMpPH/bieRU25jF8vyUywev6hk60cu15y2udRz/qJGVZu/U5jmNnb/t+IIqZvKe3QvW2jPBduvtrG9BYrM94PMibBQO3DJ17M0onzuOQzjRmrJP4sSdAvSPdByUCdMyfvIui2svV/0XwtYQv85VZrJMf6xJVFnLGqJK7cv59hwdMLbn89Kv+jF8Gp8+Hp2Ra2jwrT5LPzj38Yz+jNYwQzcxv4dfoEHv+qjN6NGqv6i8jCu/xJTVSvymR3bCyE4RXQ6oLHz7Jw52vxvZJ3pwpkXz9Hf2Il5N375jngtGoeGNKLMc/p5JqG7MuTJRNWdVQo599h44Vn/CTV2qjMhoJufJX0qhvy8dvrePnhaH36aLxgthkaO//26LvIa5Q88VTiWOX/hg+gNquUk1dFn8ufLrRworUv7/UpZ4A7ifvnxwe//3qOhcI6uOBLHb/dCM9YXRr9+9VSsio+G29FRjKFzV7CKToX/9rQ/n/e2sjgeUb4eO5xFt48PpXRbhebM+p5+f2Ocq7uNZBRu7YmNA5tbOsJFTmCCTHTaJ1ZQQKNDrxOI7S2ZKLOc0fZefmh6DV8Njs/OuN+/m1/gJG126hbn9b5RfYDh0cPoF3dXN5jJ1XexAHknTnRg5N0nUBWdMXd9ekWzNA7NZlGF6ml3fhUm/IHOG15vPIHWDbEKP/bYXZ0i4ibBldmhp0chfHB5C9Gd3yMbZUjaLdQmm+c92a/jnHRFK1jLbLl5+GeciMAZ7g9XQoF6Bka+daolg2kOHZzNKS77KSlxxue+RMErTH2KZcwLUl0UP4pvQW6VaJZBfZ2sjnC8YPcfquTedM7r2Ztvaoq/zBsMc8iZDWUMMDArGZmeH1sMsMveo8UAlZDOYadkrtCV2BPMhpRSU9juy6I+8BGqyU6gbXGHFPzOSHQ7jGdVWa824Y0KAyF43p9ywZbWBiegtTtBByGrJU5sKOHcZ3knCDZQ3Y/Mreun3Hs4hECX1pHpdyQKjjG54tLJW9xdC3uD2BL0gnIFCpj2slWx4C4YzYWGtNZAZqtxjPe2Gffuxz/myw4Vi8jZI8vI24KqfkuhG6Lq2PtWZ4zjtZ2bXZDX8E3vikkScnW1I6B94Y0wVcjjfK/NP/akzRszo7PbWH/4QBoY6OVOssWbTfeZAt6OI3yJC+hBB9o+XhyHg3WtA7Kv6JfvKFI9cFng+OHiPPGGHG4zb2MSpdi1QjZBOU50Zf95uCjcFgtLNGHExq4+4/cyN1ZoC5yeBiAGM6/w0ZNVueVsSq6KJCTkp6hMiPeSwqYBqA1SXD5LTYeOm/v+rn/PdbCBZedxlOnGA+3IcYA3xYIMOyCSvKK4jXiotGCip/0IPBjF2ef8We+6BttcJlpuTx2loXLbrayfEj8437u5Bew9j+xgwz2vHw29ziJCf6nONbn79R7juVC0YTFHq3wWsHuJwmnJ9lJSo5WoAtut/LcbGucwVxy5jLcSR3fRc/JTWy7wkjTkK7HP99ejTLOkzx+RAHvHmXhpafP4Z7fPhjZ7ncZ124LVa3xH4W1tU9kf2sSLJhgYdgFlZxv/4Qnqmv5riiNy262suzWhwmaXxFb7yzkBW02/UOvcPa1M/i/U+OnBmZqRsN0x8wlqcg17smqEwkrtdGn1JChMVXQU9PwOY15OQ2psK0XzAvNBmnHFTSedWW24LYrLfzo/KtwZYXJn9DCN6dc0OGZtfHG2AFcfpOV50620JDTcVV6YxoUhsNxmRmsuzEAz18SLeN351yN1S4JkEGFsSCWsAUWi6PizznnSSb/5gRsUtJoM17A8sGCS3/j5NJfT+H8O2ycf4eN96Z03g4zzDbwz9MsvHiilTzzOd947YjIMVUdE7mCltQh7BJLlWMA1XVXRX6ff7sVzSpYoE/BrWWCEATbNemGNKjOElz9W1gy1JDZlqR3yMYNUGUbRsblDtIGRttwjxjHqTrHjgxn0OzsOGPgFzN/w6O9b8WbIKOtdmIr599h41c/MepZtt/Ft9zIrTGr8EdlPc+pZz1IuTDWLWVYjGd2wznTuOQWK+ffbuW3rz7E5ntP4b9J53F2+kP87MTbOn1WdmcXps7tgcPCACRKBdHhGNNTafMywq3DEa5U6jCs6uqBxvaoATD+Nuwmg/Li4cY5KfnRqQ5+B8jCj0EI9HBynAGosWciBFjGnR3Z1pgCPqegUPfQQDoBm5N6Z9Ryp2f3QgpBwNHxJnOSM5HBjrEdR1E/ttZ4cNP5R1Tak5QZYrFzYuR3flbnH/9Ymj+M9CQblpChxDcVgjS9ndj4bO+8LFoSeGvWcXNINSOcvhxjoGBHH0PWu14xlJXXbOR2n9G4wnqYvJRoiCYSpzaRoXTsMd+jbZsxIgQM0o3FSnrTBAIOgS+YRqrfuE5jquDHE40PyUvNRVWOMXr42Vjj/AybUQFaRdQba1MSu7IEk7T40Ub7TmOaV0MapOlGKo+6DPhmuEAKgdQdSN0RmYK3MwcQKfiCQyJl5EycSmcELQ78TgFCkO4q7LC/IU3QQ9OQMWMWbZ7shj4dDqdFNzwiYRc0pg7GJx1UBrKpNL1vmw4NlvgTTxnRl9y+UzjG549MKw3aIOyyIEx/qm/SmMjsofbI3oKUnkabKTeNaYamk6LrNNmi9bkypgcw2MwUGm6esNtPHza40mlxxuTREQItYDoz0mjc4XYGoMk8fKo/gDDj/rYkjQbSO5TvsznJTrbRQ4sqz14x31+ozbNj0Y3zhgeCWJOix104axQAfmvUglkyjGOP9Ro9k0rbMAA2D50CWAg727VhYaFtdCDfnAYd8Iw19JoQpNhTEEKQneKgxh2k2dEup1AM1vbd733gsDAA9Qmmub506kt8mhL1XOwvP8lPbzTe/PGuf+LbeQlOm/H7yhuszPj323xx5tsE2wyAqbgaExiAracXUvPca9w39gYevfpPZN5irKq0FxTw5px3GZ1tZDjsl5lLQ4xC3Cl6MNn/JGLqzwCoyIFfX2fl6epsemitNJLG2D6ZXHCKcX7QCqk5+XRGuiM97jN7YQt8euttZP74x0Z6a5sT7eZo3HrIsqXUzTByIS3pNZzs+QsYdF0BA06r5v2xv+d2y7WRY/um9Y271vZRxnXeHTGAe6f8hBSHkXzu9l/CHy+KtqhtMSHT7FQnNQ0dvVnLWX9lzcRHAfDn92TQWbvYNjTeUrQZDpuZETWkh8hNib6M1tHD447XwxkRA9D6578x77j4qlk8+z+01p3GnybM49viEBlmAjLv5BQePG8MS++cxU2zxhK0C372ayvPn2Scn549GKk5aYkJAX02RnDNr6y85N/JTc2J52U3J4NrmpHd8o6fWHn5eKM8qTtAt0fGZCpzBP2SJkKMwp585vHU/ePeDmU+ecM/kObiJSktJFs61o2GVDjF8yShpOg+OfZkrjr1Jl45Pl7z+e2wIHQMef/9F0M+/4R3bj6FPw2ay7LAIOpjdJ9X5HLR6bdEfp88Ih/GX8Zd57zP0JyRke12qwWLMIzN1NwzqGo11r/aph9Hjy8/oujzTxn89dd8fMOLTO/5GPdd9RdKehvtI23m70lK6kGrPWpQd9RGvdfjvT4+LavAW3sqr8z+H2/d/Tx3/IrIVO823PYkWhzRurTw3C/wbv+l+ctMpmYz4nKrBk/mwlPvRjMnM1zZ3MrDDYYBf8p+JivP+ZyyF9/j5unRT8w9esVROK2Qrktyt17Oh+U7yYj5pKw33cHlk8cCcILXS98zmxmyfBmDvljENecezbd3zOKyEwwl77U5GfzBmwz5URWpUuLe8ju8dWdw4al389DIs0lz2cjpEQ1ZvPvLaay5+2QuNL9e+LLvXNzFd6D7+kWOsZlfESutM3ooo4dGHblNZxXFPSvLgH2e/BMto9sl7AdCCca6+qb3pUev0ZHfaYX98ZjhiHRHBkgbLruF3qm9cScLevUYQlb2QEI2ozCH2W0Ot81qsUUv4k1K4pipo8iw9Cej72BsvYsAyDj7LAZnD+Anow2lN7rHqLgewFa9gFqysKQbrWvlIMOz722VpOnNNMg0MpLs5PYzXqjfYSj5zki1p+IaHlWEy4YImgqGImw2tta66Z+TgjUtGrqwpqVhyTV+l6Xkk9OnAPvYE3Gma/hSCqnVjYbRXFCEc9DAuGvZswzlmjJ5PCGrjRSnDfQQ6XYt+owAzSqwpBk3nZ3soMEZvb7DLFMkpVDQYxAAvTNHYE/SqbPE32dJgVFmUj8jHDYgcwDZKdHwjG94UfzD0J1U5fcHoGDciEiPJLK7//GAwO1JZllpA5r5PrOHj0IIQV6aiz4ZhqfYkiLQzSRsvVJ7ooeyGTXIuN7mAmMwsilVkKXrOIomkghpEQzsYYRXWpNF9BlJB1LaKTPtSWMqHNVzRuQ8r3SS6rIxcMKUDmWefMxQ0A25ZTiVFHqzxcxrkTxlSqS8Vhn/LG3CyrCxYyO9WtcII8zic4CmJ5EzejqWnALSXHaGDBiI1J1xjgtYaLJHLfugvFSwWMjrOZS8CUZvpSVZMCFvAk5pKJweyfnUOY1zkvN6kNujL0k9e2HLzuKnJ43Fb03ny/pom0pL702qMw2Loz7S685LN8qqS4f+wRA9NKNNjsrviys7i1SLJW5GHwBCxHm9ealZIE2P2wyZ2Mx8XTtDVpqdUaeihOHk2I16XpaST3ZWNqOH9I4rLzsnA9HbUJyeYC8K2+XwslntDM4xnKcTPD4sdivW1FTseXkIIeiZ4SI1w2gfLY5kLJl5WB2SupzJSC0VsOJNTqPap5OX5qR3YdTxGFOYSbrLHnFc/TgZlW9cS+jxnurI3kYduG6msQrflqSx3OxdtGFJ6rx30FUOi1lAAOLcen6VG41bZzmz4OhfAkaSpfSMqCKym+Egl83Ky6e9HPl4B8CwgnHIiuXcNPk3JKUXM790PosfPJ/LJ1zN1hNPAuCk0X2wWy3Mu/ZoMpLtJKe56PvCCyRPNirGyUUn87zrecbljWPNkPPhb5cC8A/tLOO6Q4fS75WXuagomx+9+UsKXbWIVj+NMpW+2UnY8w3vzeuENEfno/hOq5O8224l/YzT0Szwt7e+4CLdmEWytdbD8F7GuYM+/yzyVa+eIkAQaHSlYbNaYMat0P84mkvzQWzm+uN/w+v3/IisHtn8+pOd3LnsJQCKZpxD61kjuP70nzFxWwNHDTACtPnhMODkyv5ncfbYqwjrYQac1YPQrl04XDa2ZPbhnqk/4fnfzMbRv3/k4xw/HjMZv/YYF42ZAVNmM3zJUvj4X1QNzua5o3ys6xVk6vW/Z/SMc3mxYQ6jc0fzwdpo/hY9zai80WmjggmPPUiwdCuFA/swP38+AgFm8rdkaTSa/62pRJcgX3gdzVbOnSOOiZR5Yr8TecjyEFo4yPCQhq33RJxWJ6f0ruWoPkOZm3Qbf9sYpi2P5afHv87x48bA348H4KtZYzj17AvI/eBqnq+yMqlfxy/ItfUA7rnESqYH7pn2R84eeDZ9neWc+M6DNMo0ljpsJDl60v4zKnPGFvB+ZS++rlmDDGeQIYbT55mn6evNwFXUn3OeOCpiaGJXpstwiCcunsDUuYLfXWblP3PuofTHPyYtI5/Ft54Rd43zJ/ehMjyUD3eTMVjEGNe8G28kbdYsHuznol96Py57ZiWVpX0YNnUU/3YZyeesyfG9u4wkO3OvmcqOei8PbEzFG3aT5kgzwhdWP7+9ysob05/n3aIx1Bz3Cr/93wd84zHGf+bfOB0hBGkuO2sqbmFE08fAqrjyrUmJQ59CGMrdYjcMQsDqoCDDRdsSh0fEL3jaWos+XbA5qy93JTsoyEzi8cungvlZEEtyMpz5N3YNvZw3eo0HMZ31lc04zvdz7YIrsVqsnD7gdJ6du4Qh+lNojgSymM+v91GTwGqHaxaxpSkDXjTSec+9+ijKGrwM75VOtkWj4a+J38PPZwyk15VT2NXix+l4G90SnUDwzOWTqHUHCIR0rj3hFga6KvlMG8+lzI+KYbcnKnavOCwMgADSksLsiokZCiHiYoVtHxXPcka7VC67lZykHHKSciLbUtIzcAN9ppzNoF1vAeApzMZeEP0cXVavIgAG50eVc8rUeI9tUuHGDRQAABSESURBVE/DGEzoOZG21DSxc8mTJ0ygL0BaJtQaidgaSWNIj1SsWYasZXlitwZACIFwOEgePx4A6zu1eIMaIU2nrMHL6aONuLW9Z9R7S81IpQGoTDE9C4sViqaRtNOIk4+aMZnsfGPfyryhkfM8OaOZcKrxYY+jB5rPy2JnWDDER0D/gkn0z+gfOd6amdkmJN/2GkXSOONTe9aMaLzusvFm0uiiaRSY30ltmDyQDX3XoemC1EmTETYb4/OM+xvYI+qxtA0E1qfHKKO8DPIHGQq9d2r8GEay2zCA325roFeGi1EThiDE0LhjnFYnpxSdQntOGmwY5J7jTsa7cTlpgB7MpDljOMLsNVidGnWZWWQOH4prcZhJ/k5G3nUHUjpoTTZmTE3vPR2LxcKMIT34nTRi+laLwErixtk7Mx1qQA+nkZvqYMKA6Ad5tveKqf+6HplHbsvNJdlhNNUthQJLkjEImTliFL0z4xVUssPGjEGFvNou8WdnCLud5AkTaBu6dVhtaL4i7FYLDa50dASW9rntgYn9spnYL5u1gVN4fcvr2ISNVLvhxcqMNHqOnIQQgpxjxrPr3Upjop+wMKyn4dnarYIWLY/tliG0NwB9c+Kv1yPNSW1rANHWA3AaPYCA1U5Rbgq+zEGUNJWQk9mTlc1p9B6YhNbkIyvZqGRjB+XTllHKkpQE9iR6jjQ/jEQ/RmZBtaea2kzBlLS+OK1ONF8ROMFi7TiJpO3bEPmnGg4lBeNIl1FzP6kom0lFhoMlpaQBsOZGewLWXKP9FfTJIyPZQWayA0gHomG/nFQnOalOtlS3UpbekzIz4YrX5iTZ/BKecOx+pl9XOCwMAEhSYuJwz54cTXQ14H/vEa6rQwjB30/4O4OzBvP0p4bHb0uwkMniMhqENSsTYubmi5gPc3DMDXslXd8XXsCamQH/v707j5KrrNM4/v3V0l29pbuT9EqWTtIJCcHYCUknEDGLBEJABMISYCBomIBkmLCMLOOZ8Qg6yugIzBx1jArxiCMqqDAMZxwIoAcVkH0xYJbDyHYSUEJAnSzd7/xx3+qq7r69VlV3p+/zOadPV711b9ett+ve577vfe+9t23v/mIiBf62iq+6Wk6sKae4uYbbT6/mgcl7ubyogttX3U6MGM6v0ufdF3I3MoKV98/729i19/9oa3dMqO6+91GzYQNvV9WzcVnnDd37foPVUJUZofDTK5fT5u8hsbP2OFq6/rENj7H2D9upZC8nTz2566sAfHddK41VfR+MPvpDZ/PwhmdZtfYaPv/TIBi6dn9NHV/OGcd+koOVO/jhKVfjEgtY0FLLNxKlvN1SS92Ynq9UU1mSJGbBzW9mN47ptBfbXytm1XHj6hbu/t1Gfr2tFOYHITzhE/NJvfVf7CdBWXlf465jrJ47hXv9KOX0PY6Lk917U9+/4TIqX9tD2ze+21GWvh3oymmLuOK4GZ2mv/WEW/mrTcHORDoAxq69gPGX/W2n6Yqbm2n4wheoOC78HtcliRJc1ztwAU133tntto1dpVvXB9vb+c+rlvP+nM9RveKYHqe/ZsE1zBo7i4UNC7nj5TuC9xnT1On/8/DfLeW557/FnDnzOsoO+vX9gUnzibt25s6dwcoV8/hmajwz6yvYUftljm4NAv7uDYt57rU9XPtUG20O4rE47QQtgFQyzi0rNvH07qdpGbuIx1/5IwunjOPRnX+gsjQIYSvOfK+sNHwMal1ZHTceeyPHNAaf9abzF8MPwax7AIy9cC3J+jrGnHRSpqyHYddmxoSvfZXUzEz3zfiLLiJRU8OYj360x3pNK0l2fv9Ll13JXclnee+++0ZXC6DM34GrNFFKa0Nmb7y4uZni5qC/eenEpb60y8VVssRKUsRKS4kVFQU3mia46XgniYElZ6Z1EBYAmYHiO9obmTwu+II9Pq+MP//pPSqKKvhgzQf79T6lRXH+vP8gu/YGCV9X2X2DGCstZfZfn9+t/E/+HsWTxma+4DMOq+5ovRwMG2o1bhrJcdM4s5dlOnZ62KW4uovFYiy/7IsAlBWV8e6+dzv2CNNKiuK8OG4aMI3K4ko47dRMKIWMcMmWiMeoqShm19591PYSFL0pKYpz9oJJPPNqK786+CoJv1NQMWcCPNrGnw6ksKJeho15s+vHdwuAVLL7hmLBmZcCsDUrAF5/PzhD7vJjTus2z4L6BZTbHvaxP7jRLVB97rnEQ/bAq047tVtZx+dMhAd2yZGzQ8uzjSnxB6kh2Fs///Rep08lUpx1+FkAlCWD5cxuSQI0jS+DZZ2/ZelbRFaUFPHfTYtomjONyfNmkj4cOvHczMa1saqExqoSrn+ulPcOvEfc4QMgSWVJkprSGo5vOh6Ak+cELf2PZt2APpZ157tYDwEAsGrqqo7Hsyb67uhU92N48fJyqs44o1NZOgAqirtvUiuWL+/03IqKqD6zt7Uuo+t3ZFfZOMoXHju6AgCCC7sel1jCpav6v3cetg9YefrpHQfJ1sxcw9t/eZu1s9cCUH/9ZymeNi1krv65+eyW7knvNwDtiVLWfGRhpw0wQEWy+x7lLctuCb09YRAAQQsAoK6i/xu6S5ZMpa29nTOO6jy0sPJTV/OTgzVc1NLYw5z5t/mEzfzi9V90bByz3bKmhTGpwX1xq0uL2LV3H/WDDIC0a1bOZEwqycojfdfakmt4Y+8BFky6BHoYdnfbCZs5Z3NwA6Dsz5WMJX1Zz+ebNHz+cyQnBgl3w+IbeOT1R2iqbAqd9gcXL+L+3+4Gf3a7ZfWHf2nJlyhN9HIWlVeaDKb559UxPjFrLd+cN58Dbf07mez6U2YzobqEY5t7PwkpzGnNp7G/bT9nHt73xm1N6yRe3/MXLlkyjX//+Q42LOt7vdx84mZ++fov4fbgOv6tsxpZsWpWH3OBZQ0A6Xe3SXldcB+MI1f3a/JUMs61J85k2eF5uEh/lpKi7t8r54+LWdEoCgCAhWXHM716er+nDxsFWzp3bkefekmihE8t+FTHa9VnnZXT8p06N2Rsvd8YxGqms3FFpk86fZG9sGMAyyct71YGwT/75797q6PLpT6kBdCTqtIiPn3SEd3KG9d9nA0h0xdSc3UzzdXNoa99rKXn8xP6EvPdCnVdzmAeqKrSIq7L3nCUVNF41peDllAPF0ecX38UB/YEfYphwZYM6Y7seL/VmY3IzLEzmTl2Zo/TNtdW0Fxbwcu+RRxLZd4r7PhGmPRQwt+31HPsmVf3a560ceXFXHdi3xvVMK0NrZ1a771JJeMd39ew722YGdUzmFE9g21tQYvqpAVTqKwY2Heh312HZsEAiwG4ZMngdy57kkp071pMnzs0aloAxX6lc8W5D2saNg3h3Ty9HQTuasUR9Ty68498//Hfk4wb1aW5/4NHo96OFeTMDOZdAIev6vbSuQsn8eHp40nGfxsyWw5Xbwsx4etf453v/Qex8r67pLqqK63j+MnHc+HsC/O6TCOGb81YqoDfgxEiEc8EwMUfnsrswyqpbCrjvS0PMG79evjMZ3L7+7kuYD5MPOCHdyVy27MbFq/5C/NPPyH05bC9xZ6s+9AUvv7wDt5+fx+1Fam8b1QOdel988qSAgfjKf8WWvxPpwXnpdy7M7jh+cKGns/4zVVZaytlrf3bm+4qEUvwL0t7GHs4Cjh/Bm2sh+Gio1V2q3Xybbfl5W+OiBPB0uLJ/gVAup97+cz89rcNyjGXQaoKpnXu1rniqCtIWIKakv4dRE2rrwzqoLFq9O/dDNRVK2ZQFI8xrXbge8WD9fHZH6dpTFOnsrm1c0nFU1xx1BWdyptry7l4SecLr0kB+JO3YhFoAQBMrSnjk0vz370EI+SGMPMb4+6J9eXcteIRVi/+QN8zjGLrNv+GLS/tZs2CiXxxdfeLhYlE3csLF9H+7rs0/ehHlHzgyH7Ns3VmsPc866WtfUx5aDGznG4IMyK6gNLiA+guGa3Sw76m1QzdXq7IIeWg7zIu6f/2IlZRQbyisNfWPxTlHAAWnCnxBPC6c+5kM5sC3AGMBZ4CznfO9X72iRcvVgC8vy/4ck8cG63+TZH+cu3pg8D9X0dm/PpXvV6FNKrycQxgI5DdrroRuMk5Nx14B1gXOleIZEKjXuZPDi510TyE/dwih5S29DGA/g8asUQCC7msQ9TlFABmNgE4CfiWf27AcuBOP8l3gJ5PWeyiKKGEvnRZMw9cuYTmWjVXRcI4HwADaQFIuFxbADcDVxOcmQ0wDtjjnEtfSes1oN9n/iTjI2pQ0rCIx0x7/yK9SF9XZyAtAAk36GMAZnYysNs596SZLU0Xh0waOszIzNYD6wGOagg2/AoAEenLxG99k33btnW6xIMMTi41uBg4xcxWASmC65neDFSZWcK3AiYAb4TN7JzbBGyCYBgoKABEpG+J6moSgzxJTjob9BbXOXedc26Cc64JWAM86Jw7D3gISF8qby1wd3//ZpECQERkyBRii3sNcKWZbSc4JvDtPqbvkNRBYBGRIZOXTjTn3MPAw/7xTmBQ7TO1AEREhs6I2uKmb3snIiKFN6ICoOvtz0REpHBGVACkikbU4oiIjGojaourYwAiIkNnRG1xdQMUEZGhM6ICQEREho4CQEQkohQAIiIRpQAQEYmoERMAO9vrh3sRREQiZUQEwP+6es7e/w/DvRgiIpEyIgJgL6W8RfVwL4aISKSMiAAQEZGhpwAQEYkoBYCISEQpAEREImpEBEDMjBtOPXK4F0NEJFJGRADMbhzD+YsmD/diiIhEyogIABERGXoKABGRiFIAiIhElAJARCSiFAAiIhGlABARiSgFgIhIRCkAREQiSgEgIhJRCgARkYhSAIiIRJQCQEQkohQAIiIRpQAQEYkoBYCISEQpAEREImrQAWBmE83sITPbamYvmtlGXz7WzO43s23+d3X+FldERPIllxbAQeAq59wsYBGwwcyOAK4FtjjnpgNb/HMRERlhBh0Azrk3nXNP+cfvAVuBw4CPAd/xk30HODXXhRQRkfzLyzEAM2sC5gKPAXXOuTchCAmgNh/vISIi+ZVzAJhZOXAXcLlzbu8A5ltvZk+Y2RNvvfVWroshIiIDlFMAmFmSYOP/Pefcj33xLjNr8K83ALvD5nXObXLOzXfOza+pqcllMUREZBByGQVkwLeBrc65r2S9dA+w1j9eC9w9+MUTEZFCSeQw72LgfOB5M3vGl/098EXgh2a2Dvg9cGZuiygiIoUw6ABwzj0CWA8vf2Swf1dERIaGzgQWEYkoBYCISEQpAEREIkoBICISUQoAEZGIUgCIiESUAkBEJKIUACIiEaUAEBGJKAWAiEhEKQBERCJKASAiElEKABGRiFIAiIhElAJARCSiFAAiIhGlABARiSgFgIhIRCkAREQiSgEgIhJRCgARkYhSAIiIRJQCQEQkohQAIiIRpQAQEYkoBYCISEQpAEREIkoBICISUQoAEZGIUgCIiESUAkBEJKIUACIiEaUAEBGJKAWAiEhEFSQAzGylmb1sZtvN7NpCvIeIiOQm7wFgZnHgq8CJwBHAOWZ2RL7fR0REclOIFkArsN05t9M5tx+4A/hYAd5HRERyUIgAOAx4Nev5a75MRERGkEQB/qaFlLluE5mtB9b7p/vM7IUCLMuhaDzw9nAvxAihushQXWSoLjIOz2XmQgTAa8DErOcTgDe6TuSc2wRsAjCzJ5xz8wuwLIcc1UWG6iJDdZGhusgwsydymb8QXUC/Aaab2RQzKwLWAPcU4H1ERCQHeW8BOOcOmtnfAD8D4sCtzrkX8/0+IiKSm0J0AeGcuw+4bwCzbCrEchyiVBcZqosM1UWG6iIjp7ow57odnxURkQjQpSBERCJq2AMgapeNMLNbzWx39rBXMxtrZveb2Tb/u9qXm5n9q6+b58xs3vAteX6Z2UQze8jMtprZi2a20ZdHsS5SZva4mT3r6+KzvnyKmT3m6+IHflAFZlbsn2/3rzcN5/IXgpnFzexpM7vXP49kXZjZK2b2vJk9kx7xk891ZFgDIKKXjdgMrOxSdi2wxTk3Hdjin0NQL9P9z3rg60O0jEPhIHCVc24WsAjY4P/3UayLfcBy59wHgRZgpZktAm4EbvJ18Q6wzk+/DnjHOdcM3OSnG202Aluznke5LpY551qyhr7mbx1xzg3bD3A08LOs59cB1w3nMg3R524CXsh6/jLQ4B83AC/7x98AzgmbbrT9AHcDK6JeF0Ap8BSwkOBkp4Qv71hXCEbYHe0fJ/x0NtzLnsc6mOA3bMuBewlOLo1qXbwCjO9Slrd1ZLi7gHTZiECdc+5NAP+71pdHon58s30u8BgRrQvf5fEMsBu4H9gB7HHOHfSTZH/ejrrwr78LjBvaJS6om4GrgXb/fBzRrQsH/I+ZPemvngB5XEcKMgx0APp12YgIG/X1Y2blwF3A5c65vWZhHzmYNKRs1NSFc64NaDGzKuAnwKywyfzvUVsXZnYysNs596SZLU0Xh0w66uvCW+yce8PMaoH7zeylXqYdcF0MdwugX5eNiIBdZtYA4H/v9uWjun7MLEmw8f+ec+7HvjiSdZHmnNsDPExwXKTKzNI7admft6Mu/OuVwB+HdkkLZjFwipm9QnAl4eUELYIo1gXOuTf8790EOwat5HEdGe4A0GUjAvcAa/3jtQT94enyC/zR/UXAu+mm36HOgl39bwNbnXNfyXopinVR4/f8MbMS4DiCA6APAWf4ybrWRbqOzgAedL7T91DnnLvOOTfBOddEsD140Dl3HhGsCzMrM7OK9GPgeOAF8rmOjICDHKuA3xH0eX56uJdnCD7v94E3gQMEib2OoM9yC7DN/x7rpzWCUVI7gOeB+cO9/Hmshw8RNE+fA57xP6siWhdzgKd9XbwA/KMvnwo8DmwHfgQU+/KUf77dvz51uD9DgeplKXBvVOvCf+Zn/c+L6e1jPtcRnQksIhJRw90FJCIiw0QBICISUQoAEZGIUgCIiESUAkBEJKIUACIiEaUAEBGJKAWAiEhE/T9Lo7God8hjtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 玩家决策，不同gamma对比\n",
    "print(\"不同gamma对玩家决策的影响\")\n",
    "\n",
    "plt.clf()\n",
    "plt.plot(moving_average([sum(reward) for reward in actions_1d[0:1000]],1),label ='0.9')\n",
    "plt.plot(moving_average([sum(reward) for reward in actions_1c[0:1000]],1),label ='0.6')\n",
    "plt.plot(moving_average([sum(reward) for reward in actions_1b[0:1000]],1),label ='0.3')\n",
    "# plt.plot(moving_average([sum(reward) for reward in actions_0[900:1000]],1),label ='0.1')\n",
    "plt.plot(moving_average([sum(reward) for reward in actions_1a[0:1000]],1),label ='0.05')\n",
    "plt.plot([50]*3000)\n",
    "plt.ylim(0,101)\n",
    "plt.xlim(0,500)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "平均收益对比\n",
      "  0.05 48.39\n",
      "   0.1 48.15\n",
      "   0.3 48.81\n",
      "   0.6 48.58\n",
      "   0.9 48.43\n"
     ]
    }
   ],
   "source": [
    "print(\"平均收益对比\")\n",
    "print(\"  0.05\",sum(sum(r) for r in rewards_1a[400:500])/len(rewards_1a[400:500]))\n",
    "print(\"   0.1\",sum(sum(r) for r in rewards_0[400:500])/len(rewards_0[400:500]))\n",
    "print(\"   0.3\",sum(sum(r) for r in rewards_1b[400:500])/len(rewards_1b[400:500]))\n",
    "print(\"   0.6\",sum(sum(r) for r in rewards_1c[400:500])/len(rewards_1c[400:500]))\n",
    "print(\"   0.9\",sum(sum(r) for r in rewards_1d[400:500])/len(rewards_1d[400:500]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rewards_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不同gamma对玩家决策的影响\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 玩家决策，不同gamma对比\n",
    "print(\"不同gamma对玩家决策的影响\")\n",
    "plt.clf()\n",
    "plt.plot(density([sum(reward) for reward in actions_1d]),label ='0.9')\n",
    "plt.plot(density([sum(reward) for reward in actions_1c]),label ='0.6')\n",
    "plt.plot(density([sum(reward) for reward in actions_1b]),label ='0.3')\n",
    "plt.plot(density([sum(reward) for reward in actions_0]),label ='0.1')\n",
    "plt.plot(density([sum(reward) for reward in actions_1a]),label ='0.05')\n",
    "plt.xlim(0,102)\n",
    "plt.ylim(0,0.5)\n",
    "# plt.axvline(51)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def variance(lst, N):\n",
    "    '''\n",
    "    序列转为方差序列\n",
    "    参数:\n",
    "    lst: 输入序列\n",
    "    N: 计算方差所需元素数\n",
    "    返回值:\n",
    "    res: 方差序列\n",
    "    '''\n",
    "    import numpy as np\n",
    "    res = []\n",
    "    for i in range(len(lst)):\n",
    "        l = max(i-N+1, 0)\n",
    "        r = i+1\n",
    "        res.append(np.var(lst[l:r]) if l else 0)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不同gamma对玩家决策的影响\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 玩家决策，不同gamma对比\n",
    "print(\"不同gamma对玩家决策的影响\")\n",
    "plt.clf()\n",
    "plt.xlabel(\"Round\")\n",
    "plt.ylabel(\"Variance\")\n",
    "plt.plot(variance([sum(reward) for reward in actions_1d],10),label ='0.9')\n",
    "plt.plot(variance([sum(reward) for reward in actions_1c],10),label ='0.6')\n",
    "plt.plot(variance([sum(reward) for reward in actions_1b],10),label ='0.3')\n",
    "# plt.plot(variance([sum(reward) for reward in actions_0],10),label ='0.1')\n",
    "plt.plot(variance([sum(reward) for reward in actions_1a],10),label ='0.05')\n",
    "plt.xlim(0,500)\n",
    "# plt.axvline(51)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不同gamma对玩家决策的影响\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 玩家决策，不同gamma对比\n",
    "print(\"不同gamma对玩家决策的影响\")\n",
    "plt.clf()\n",
    "plt.plot(moving_average(variance([sum(reward) for reward in actions_1d],10),10),label ='0.9')\n",
    "plt.plot(moving_average(variance([sum(reward) for reward in actions_1c],10),10),label ='0.6')\n",
    "plt.plot(moving_average(variance([sum(reward) for reward in actions_1b],10),10),label ='0.3')\n",
    "# plt.plot(variance([sum(reward) for reward in actions_0],10),label ='0.1')\n",
    "plt.plot(moving_average(variance([sum(reward) for reward in actions_1a],10),10),label ='0.05')\n",
    "plt.xlim(0,3000)\n",
    "# plt.axvline(51)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "平均收益对比\n",
      "  0.9 48.4075\n",
      "  0.6 48.6275\n",
      "  0.3 48.495\n",
      "  0.1 48.43\n",
      " 0.05 48.405\n"
     ]
    }
   ],
   "source": [
    "print(\"平均收益对比\")\n",
    "print(\"  0.9\",sum(sum(r) for r in rewards_1d[100:500])/len(rewards_1d[100:500]))\n",
    "print(\"  0.6\",sum(sum(r) for r in rewards_1c[100:500])/len(rewards_1c[100:500]))\n",
    "print(\"  0.3\",sum(sum(r) for r in rewards_1b[100:500])/len(rewards_1b[100:500]))\n",
    "print(\"  0.1\",sum(sum(r) for r in rewards_0[100:500])/len(rewards_0[100:500]))\n",
    "print(\" 0.05\",sum(sum(r) for r in rewards_1a[100:500])/len(rewards_1a[100:500]))\n",
    "# print(\"   0.6\",sum(sum(r) for r in rewards_1c[400:500])/len(rewards_1c[400:500]))\n",
    "# print(\"   0.9\",sum(sum(r) for r in rewards_1d[400:500])/len(rewards_1d[400:500]))\n",
    "# print(\"random\",sum(sum(r) for r in rewards_sp[100:500])/len(rewards_sp[100:500]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "决策方差对比\n",
      "  0.9 5.766693750000001\n",
      "  0.6 5.265\n",
      "  0.3 6.40484375\n",
      "  0.1 7.288443749999999\n",
      " 0.05 6.943943750000001\n"
     ]
    }
   ],
   "source": [
    "print(\"决策方差对比\")\n",
    "print(\"  0.9\",np.var([sum(r) for r in actions_1d[100:500]]))\n",
    "print(\"  0.6\",np.var([sum(r) for r in actions_1c[100:500]]))\n",
    "print(\"  0.3\",np.var([sum(r) for r in actions_1b[100:500]]))\n",
    "print(\"  0.1\",np.var([sum(r) for r in actions_0[100:500]]))\n",
    "print(\" 0.05\",np.var([sum(r) for r in actions_1a[100:500]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.50644375"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var([sum(r) for r in rewards_1d[100:500]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.        0.4823123]\n",
      " [0.4823123 1.       ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f280da2eac8>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"γ\")\n",
    "plt.ylabel(\"Reward\")\n",
    "plt.xlim(0,1)\n",
    "plt.ylim(40,60)\n",
    "xData = [0.05, 0.1, 0.3, 0.6, 0.9]\n",
    "yData = [48.30 , 48.31  , 48.33 , 48.56 , 48.35]\n",
    "print(np.corrcoef(xData, yData))\n",
    "plt.scatter(xData, yData, s=40, c=\"#ff1212\", marker='o')\n",
    "parameter = np.polyfit(xData, yData, 1) # n=1为一次函数，返回函数参数\n",
    "f = np.poly1d(parameter) # 拼接方程\n",
    "plt.plot([0,1], f([0,1]),\"b--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.         -0.88409189]\n",
      " [-0.88409189  1.        ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f280da33278>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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79rvJZnfy2GOn5o9PJutIp+eyYMHVzJ27kr6+XfkBAkEABf00yWQdwYz8IiJjb2+d6PcS9Hf8YDw+fbCQ/Xki4XjuAM/lMmzf/tt8uGQywSCAhoZ3M3v2u9i1awOPPHLk645bvPgG5s37e7q7N7Fp0yeHhEs63URd3bFUVc0sw08kIuNFXJ3op0cvaWRmtoRgVFe/g4FPu/v/HbTPW4AfE0yfAvBDd4916PAUpnAoh8b5EZElEmlmzDhzxO21tYdz8smv5ftn+vtopk07BYDe3m10dT1OJvNz+vp25o9buvROZs06n23bfsXTT39g0PDmuVRXNzFnziXU1Cwgm91JLtdNVdUszIq591REDgQlnyrW3Z8mvJPdgnGwL1H4oVX3u/u5paytUpkZVVX1VFXVM3nyEa/bPnVqCyee+DQAfX278lcxkycfBUAqNY36+lPDprOn2L79XrLZ7cyYcTY1NQvo6LiDZ575IGYpqqrm5INm8eKvUVMzn127NtDdvTF/dVNVNZtEQrMQi0x05f4tPwP4S/+MvxK/ZHIytbWHUls7cLU1derxTJ367SH79fV1E4yfgGnTTubQQ//fkD6aPXteIJFIA9DRcQcvvDB4XEWCqnQDDcffzdyqw+je8iu6utYNuboJmtMWqI9GpIKVO0AuILjbvZCTzOxxoA34mLs/WWgnM1sJrARobm6OpcgD0eAhypMnH1Hwyqbf/Pn/wMyZK8hk2unuaeUXmTVszjzOLanz6SPH53c0saj1j0Auf4xZNaedFox8e+65T9PZ+eCQcKmpOYhZs84HgkEHw2/aFJHyK1uAmFkaOA/4VIHNjwIHuXuXma0A7gQWF3ofd18NrIagEz2mcmUvqqpm5jvjP8EnWMNmuqkBgill/uXQl7nokKv5bObqfB9NNtuZv/owS5LN7mD37qfIZNpxz1JTc0g+QB5//G2DrmD676FZTnPzxwDo6nqcRGIS6XQTqVRd6U+AyAFqxFFYsX+w2fnAle5+VhH7Pg+0uPvWve23P6OwZP910cUSlgx5sFa/4MbMp0cd4eaeo7f3VbLZHflmtra2b7Jr1+NDRqDV1h7OsmXBY2keeuhwuruDPp5EYjLV1U3MnPnXHHrol8PjV5NITBo0Am0uqdQ0NZ+JENMorBK4kBGar8ysEXjF3d3MTiCYdv7VUhYn+66YZ6OMNtLNLEE63UA63ZBfN3fuB/Z6zMAsAW35EWiDhydv3Hg1udzQ25caG9/P4YffhLuzYcN7qKqalZ/nrLq6idraI6mpWTDajyxyQCtLgJhZLXAm8KFB6y4HcPcbgXcBV5hZlmAm4Au8XJdKUrRyPRul/27+kbzpTQPB0t+EVlt7OAC5XDddXY/R09NOX9+O/DHNzddw8MGfp7d3G2vXLn/dTZozZ66gru64cPaBVwtOriky0ZUlQNx9NzBz2LobB72+Hri+1HXJ/hmvz0YpNLlmv2SylhNO2AAEjwPIZF4mk2knnQ7Czj1Lff1byWTa2bPnL+zY8Tuy2Vepqmqgru44du/+M2vXLgcSpNNz8kHT3PwJ6utPJZPZQmfnHwb13zRqzjOZMMo9CksmmEp+NkoyWcukSQczadLB+XXp9GyOOOKWIfvlcj30XxCn040sXnzDkMcEZDIvkcv1ALBz58OsX/+OIcdXVTVw1FE/pL7+FHbu/CNbtnx/0Bxo/c1o83UvjYx7ZetEj4M60ceP8Tw1TClls110dz89KFyCoGlu/jiTJh3Cyy/fylNPXQbDmv5aWp5gypQ30NFxB21t3xgWLsEggWSyFvecZgeQ/VKpnegygY3nqWFKKZWaQl3dcdTVHVdwe2PjpcyZczG9vVuH9NHU1CwCgntgcrldbN9+fzjEOQPAySdvI5ms5bnn/pmXXrr+dX00Bx+8ikQiRXf3JnK5TDiL81SNPJMxpQARKbNgGv85pNNzCGf5yZsz5z3MmfMeANydbPY1enraSKXqgWCWgL6+Xfng6ex8iL6+Tg455F8BeP75a3nlldsA8vfK1NYuYdmy4OFoW7feRTb72pCrm1RqhoJGiqIAEakQwZxnM6iqmpFfN3Pm25k58+0jHjN//keZMeOcIX00ZgO/9q2tX2H79v8acszkycs4/vjHAdi06Z/DgBnoo6mpWcTkyYeP7Q93AJoIzbwKEJEJrK5uOXV1y0fc/oY3/GTIDZrBkzZr89u7uv5IZ+cDZLOv5ddNn34WRx8dPK3z0UdPJpfrGfKYgKlTT2TmzHMA6Ol5maqqWRoQMEiWLNdwDbdxG0mS9NHHxVzMdVxHqsL+JFdWtSIypgpNrjnYsmU/BaCvb09+iHP/JJoAU6YczZ49z7Fnz2Y6Ox+kt3cLjY3vZ+bMc3B3HnywGfcsVVWz8/OczZ59AY2Nl+CeY+vWHw+6umkkkaguyc9dTtdwDWtYM2TGhjWsAWAVq8pVViQahSUiYyaX6yWX20MqVUcul6W9/ZvDnlPTxpw5F7FgwdVkMh384Q9zhhyfSs1g0aLPM2/eFfT2buPFF784ZA606ur+Ic6VGTRjMd3PWNMoLBHZq1K1tycSVfkbJROJFPPmXT7ivqnUdI477tFh99C0M2lScDXU0/MSL774Jdx7hxy3ZMlNNDW9n66uP7Fx41WvG4FWX3861dWNuOcAG1cDAsZiup/xRAEiMoGN5/b2RKKKurpjgGMKbp8y5Q2cdtoeentfHfKkzfr6UwHI5faQy/XQ2fkAmUw7uVzwv/ply35JdXUjW7feyYYNl7zuSZvz5/8TNTXNZDId9PZuDWdxri9J0JRrup+4KEBEJrBKb28fOrnmsiHbpk49nmOP/T3QP8R5B5lMO9XVwSSYNTWLmDv3g/n7a7q6HuXVV9toavogAB0d32XjxqvCz6nOB81RR32f6uq5dHY+zK5dTw7qo2na78c6j9fpfqJSH4jIBDUe29vHA3fHzOju3kRn58ND+mcymXaWLr2TVKqOv/zl47z44heHHGuW4pRTOkkmJ9Hefgs7dz40rI9mLnV1x+718wdfFfZP91POq0L1gYjI60y09vax0t9UNXzes+EWLvwsc+dekX+McybTTm/vlvzTOnfv3kBHx/fIZgeeNJFKTeeUU7YB8MwzV9DZ+dCQ/plJkw6lsfESVrGKT/VeyZbkLpoSCyo2yBUgIhPURGtvL7VksoZJkxYxadKigtsPOWQVhxyyilwukx/inM3uzG+vqVkUPqemnZ0719Hb28HkyUtpbLwEgE1/upDOzj/QUdWQv4KZNu0kFi68FoDXXvsNiURNflsyWRP/D72PFCAiE9REa28frxKJNDU1zdTUNA9Z39z8cZqbP55fzuWy9PV15pfnz/9Hdu8+a8gcaN3dm/Lbn3rqffT0bM4vp1L1zJ59IYcd9jUAnn/+cySTU4bcxFldPW/IjaBxU4CITGCVPL3+RJNIpEgkBqahmT373XvdP5gl4KUwXIJmtMmTlwLBo583b171uidtzp17OYcd9nVyuSxPPHEm6XTjsP6ZFmprl+QfR7C/I88UICITWIoUq1jFv/AvFT/v0oFmypQ3MGXKGwpuM0tw6qk785NrDtxDcwgAfX1duPfR2flIOMR5NwCLFn2egw66hp6eVh5++DDS6bn7VaMCROQAoOn1J56hk2suHbKtqqqeY465DwhGnfX17SSTaSeZnAYEzW5z515JJtMObCKqsgWImT0P7CR4kk52+DAyC66tvgqsAHYD73X3R0tdp4hIJTOz/GOd+6XTczj00H8Nl74T+b3LfQVyurtvHWHbOcDi8OtE4OvhdxERGQfG87Mwzwe+5YEHgXozayp3USIiEihngDjwSzNbZ2YrC2yfB7w4aLk1XDeEma00s7VmtnbLli0xlSoiIsOVM0BOdvdjCZqqrjSz04ZtLzS+7HXzrrj7andvcfeWhoaGOOoUEZECyhYg7t4Wfu8AfgScMGyXVmDBoOX5QFtpqhMRkdGUJUDMbLKZ1fW/Bs4C1g/b7S7gEgu8Edjh7u0lLlVEREZQrlFYc4AfhXdBpoDvuPvPzexyAHe/EbibYAjvRoJhvO8rU60iIlJAWQLE3TcBRxdYf+Og1w5cWcq6RESkeON5GK+IiIxjChAREYlEASIiIpEoQEREJBIFiIiIRKIAERGRSBQgIiISiQJEREQiUYCIiEgkChAREYlEASIiIpEoQEREJBIFiIiIRKIAERGRSBQgIiISiQJEREQiUYCIiEgkChAREYmk5AFiZgvM7F4z22BmT5rZVQX2eYuZ7TCzx8KvT5e6ThER2btyPBM9C3zU3R81szpgnZnd4+5/Hrbf/e5+bhnqExGRIpT8CsTd29390fD1TmADMK/UdYiIyP4pax+ImS0EjgEeKrD5JDN73Mx+ZmZHlbQwEREZVTmasAAwsynAD4CPuHvnsM2PAge5e5eZrQDuBBaP8D4rgZUAzc3NMVYsIiKDleUKxMyqCMJjjbv/cPh2d+90967w9d1AlZnNKvRe7r7a3VvcvaWhoSHWukVEZEA5RmEZcBOwwd2/PMI+jeF+mNkJBHW+WroqRURkNOVowjoZ+DvgT2b2WLjufwLNAO5+I/Au4AozywLdwAXu7mWoVURERlDyAHH33wE2yj7XA9eXpiIREYlCd6KLiEgkChAREYlEASIiIpEoQEREJBIFiIiIRKIAERGRSBQgIiISiQJEREQiUYCIiEgkChAREYlEASIiIpEoQEREJBIFiIiIRKIAERGRSBQgIiISiQJEREQiUYCIiEgkChAREYlEASIiIpGUJUDM7Gwze9rMNprZJwtsrzazO8LtD5nZwtJXKSIie1PyADGzJHADcA5wJHChmR05bLfLgNfc/VDgK8Cq0lYpIiKjKccVyAnARnff5O4Z4LvA+cP2OR+4NXz9feAMM7MS1igiIqNIleEz5wEvDlpuBU4caR93z5rZDmAmsHX4m5nZSmBluNhjZuvHvOLKNIsC5+sApPMwQOdigM7FgCVRDyxHgBS6kvAI+wQr3VcDqwHMbK27t+xfeRODzkVA52GAzsUAnYsBZrY26rHlaMJqBRYMWp4PtI20j5mlgGnAtpJUJyIiRSlHgDwCLDazRWaWBi4A7hq2z13ApeHrdwG/cfeCVyAiIlIeJW/CCvs0Pgz8AkgCN7v7k2b2WWCtu98F3AR828w2Elx5XFDk26+OpejKpHMR0HkYoHMxQOdiQORzYfqPvYiIRKE70UVEJBIFiIiIRFJxAaJpUAYUcS6uNrM/m9kTZvZrMzuoHHWWwmjnYtB+7zIzN7MJO4SzmHNhZn8b/tt40sy+U+oaS6WI35FmM7vXzP4Y/p6sKEedpWBmN5tZx0j3ylng38Jz9YSZHTvqm7p7xXwRdLr/BTgYSAOPA0cO2+fvgRvD1xcAd5S77jKei9OB2vD1FQfyuQj3qwPuAx4EWspddxn/XSwG/ghMD5dnl7vuMp6L1cAV4esjgefLXXeM5+M04Fhg/QjbVwA/I7gP743AQ6O9Z6VdgWgalAGjngt3v9fdd4eLDxLcczMRFfPvAuBzwP8B9pSyuBIr5lx8ELjB3V8DcPeOEtdYKsWcCwemhq+n8fp70iYMd7+Pvd9Pdz7wLQ88CNSbWdPe3rPSAqTQNCjzRtrH3bNA/zQoE00x52Kwywj+dzERjXouzOwYYIG7/6SUhZVBMf8uDgMOM7Pfm9mDZnZ2yaorrWLOxWeAi82sFbgb+IfSlDYu7evflLJMZbI/xnQalApX9M9pZhcDLcCbY62ofPZ6LswsQTCr83tLVVAZFfPvIkXQjPUWgqvS+81sqbtvj7m2UivmXFwI3OLuXzKzkwjuP1vq7rn4yxt39vlvZ6VdgWgalAHFnAvM7K+Aa4Dz3L2nRLWV2mjnog5YCvyXmT1P0L571wTtSC/2d+TH7t7r7s8BTxMEykRTzLm4DPhPAHd/AKghmGjxQFTU35TBKi1ANA3KgFHPRdhs8+8E4TFR27lhlHPh7jvcfZa7L3T3hQT9Qee5e+RJ5MaxYn5H7iQYYIGZzSJo0tpU0ipLo5hzsRk4A8DMjiAIkC0lrXL8uAu4JByN9UZgh7u37+2AimrC8ninQakoRZ6LLwJTgO+F4wg2u/t5ZSs6JkWeiwMiOcCdAAABJklEQVRCkefiF8BZZvZnoA/4H+7+avmqjkeR5+KjwDfM7J8ImmveO0H/w4mZ3U7QbDkr7PO5FqgCcPcbCfqAVgAbgd3A+0Z9zwl6rkREJGaV1oQlIiLjhAJEREQiUYCIiEgkChAREYlEASIiIpEoQEREJBIFiIiIRKIAEYmJmd1jZlcPWv60mX28nDWJjCXdSCgSEzObRPAMiqVAL/AMcNpo00OIVIqKmspEpJK4e7eZ/QZ4G9ANPK3wkIlEASISr+8RTCOfAv6jvKWIjC01YYnEyMySBE1XaeCQ8Ml4IhOCrkBEYuTufWEzVrfCQyYaBYhI/F4CuspdhMhY0zBeERGJRH0gIiISia5AREQkEgWIiIhEogAREZFIFCAiIhKJAkRERCL5/yy2w/6BsffYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"γ\")\n",
    "plt.ylabel(\"Variance\")\n",
    "plt.xlim(0,1)\n",
    "plt.ylim(0,20)\n",
    "xData = [0.05, 0.1, 0.3, 0.6, 0.9]\n",
    "yData = [8.25 , 8.70  , 7.23 , 5.85 , 6.27]\n",
    "print(np.corrcoef(xData, yData))\n",
    "plt.scatter(xData, yData, s=40, c=\"#12ff12\", marker='o')\n",
    "parameter = np.polyfit(xData, yData, 1) # n=1为一次函数，返回函数参数\n",
    "f = np.poly1d(parameter) # 拼接方程\n",
    "plt.plot([0,1], f([0,1]),\"y--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.         -0.90213924]\n",
      " [-0.90213924  1.        ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f280c56d978>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"α\")\n",
    "plt.ylabel(\"Reward\")\n",
    "plt.xlim(0,1)\n",
    "plt.ylim(40,60)\n",
    "xData = [0.05, 0.1, 0.3, 0.6, 0.9]\n",
    "yData = [48.80 , 48.39  , 46.43 , 44.43 , 44.98 ]\n",
    "print(np.corrcoef(xData, yData))\n",
    "plt.scatter(xData, yData, s=40, c=\"#ff1212\", marker='o')\n",
    "parameter = np.polyfit(xData, yData, 1) # n=1为一次函数，返回函数参数\n",
    "f = np.poly1d(parameter) # 拼接方程\n",
    "plt.plot([0,1], f([0,1]),\"b--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.        0.8783419]\n",
      " [0.8783419 1.       ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f280c425ef0>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"α\")\n",
    "plt.ylabel(\"Variance\")\n",
    "plt.xlim(0,1)\n",
    "plt.ylim(0,60)\n",
    "xData = [0.05, 0.1, 0.3, 0.6, 0.9]\n",
    "yData = [4.53 , 7.19  , 27.92 , 49.33 , 40.68]\n",
    "print(np.corrcoef(xData, yData))\n",
    "plt.scatter(xData, yData, s=40, c=\"#12ff12\", marker='o')\n",
    "parameter = np.polyfit(xData, yData, 1) # n=1为一次函数，返回函数参数\n",
    "f = np.poly1d(parameter) # 拼接方程\n",
    "plt.plot([0,1], f([0,1]),\"y--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.         -0.99888691]\n",
      " [-0.99888691  1.        ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f280c4bea90>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"ε\")\n",
    "plt.ylabel(\"Reward\")\n",
    "plt.xlim(0,0.12)\n",
    "plt.ylim(40,60)\n",
    "xData = [0.01 , 0.02 , 0.05  , 0.1]\n",
    "yData = [49.75 , 49.57  , 49.06 , 48.34 ]\n",
    "print(np.corrcoef(xData, yData))\n",
    "plt.scatter(xData, yData, s=40, c=\"#ff1212\", marker='o')\n",
    "parameter = np.polyfit(xData, yData, 1) # n=1为一次函数，返回函数参数\n",
    "f = np.poly1d(parameter) # 拼接方程\n",
    "plt.plot([0,1], f([0,1]),\"b--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.99783152]\n",
      " [0.99783152 1.        ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f280e821588>]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"ε\")\n",
    "plt.ylabel(\"Variance\")\n",
    "plt.xlim(0,0.12)\n",
    "plt.ylim(0,10)\n",
    "xData = [0.01, 0.02, 0.05, 0.1]\n",
    "yData = [0.80 , 1.39  , 3.20 , 7.09 ]\n",
    "print(np.corrcoef(xData, yData))\n",
    "plt.scatter(xData, yData, s=40, c=\"#12ff12\", marker='o')\n",
    "parameter = np.polyfit(xData, yData, 1) # n=1为一次函数，返回函数参数\n",
    "f = np.poly1d(parameter) # 拼接方程\n",
    "plt.plot([0,1], f([0,1]),\"y--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1. -1.]\n",
      " [-1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "xData=[3,2,1]\n",
    "yData=[1,2,3]\n",
    "print(np.corrcoef(xData, yData))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class MinorityGame_1(gym.Env):\n",
    "    '''\n",
    "    Minority Game, we have some agent, every agent can choose 1 or 0 every day.\n",
    "    In midnight, all of the day to make a few choices of the agent to get +1 reward.\n",
    "    '''\n",
    "\n",
    "    def __init__(self, env_max=2000):\n",
    "        '''\n",
    "        环境初始化:\n",
    "        玩家数固定101;\n",
    "        env_max 环境承载量，选择1能获取收益的最大人数，默认为50;\n",
    "        action_space 动作空间，大小为2，玩家只能选择0或1;\n",
    "        observation_space 观测空间，这个环境使用2，玩家立足于上一次博弈的状态;\n",
    "        '''\n",
    "        self.env_max = env_max\n",
    "        self.action_space = spaces.Discrete(2)\n",
    "        self.observation_space = spaces.Discrete(2)\n",
    "        self.seed()\n",
    "\n",
    "\n",
    "    def seed(self, seed=None):\n",
    "        '''\n",
    "        设置seed\n",
    "        '''\n",
    "        self.np_random, seed = seeding.np_random(seed)\n",
    "        return [seed]\n",
    "\n",
    "    def step(self, action_4001):\n",
    "        '''\n",
    "        每一步博弈：\n",
    "        1. 检查输入是否合法\n",
    "        2. 统计选择1的人数allpick，allpick不超过env_max则1获胜，否则0获胜\n",
    "        3. 返回S(玩家本回合动作), R(所有玩家的奖励列表), done(False，无尽博弈)\n",
    "        '''\n",
    "        assert len(action_4001) == 4001\n",
    "        assert all(map(lambda x:self.action_space.contains(x), action_4001))\n",
    "        allpick = sum(action_4001)\n",
    "        reward_4001 = []\n",
    "        for action in action_4001:\n",
    "            if action == 1 and allpick <= self.env_max or action == 0 and allpick > self.env_max:\n",
    "                reward_4001.append(1)\n",
    "            else:\n",
    "                reward_4001.append(0)\n",
    "\n",
    "        done = True\n",
    "\n",
    "        return action_4001, reward_4001, done, {}\n",
    "\n",
    "    def reset(self):\n",
    "        '''\n",
    "        重置环境，每轮第一次博弈给所有玩家一个随机状态\n",
    "        '''\n",
    "        # return [0]*101\n",
    "        return [random.randint(0,1) for _ in range(4001)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "def play_qlearning(env, agent_4001, episodes,render=False):\n",
    "\n",
    "    episode_rewards = []\n",
    "    episode_actions = []\n",
    "    # 初始化S\n",
    "    observation_101 = env.reset()\n",
    "    for _ in range(episodes):\n",
    "        # 各智能体根据环境选择动作\n",
    "#         print(len(agent_4001))\n",
    "        action_101 = [agent.decide(observation) for agent, observation in zip(agent_4001, observation_101)]\n",
    "        # 执行动作后得到环境奖励和新状态\n",
    "        next_observation_101, reward_101, done, _ = env.step(action_101)\n",
    "        # 为所有智能体更新Q表\n",
    "        for agent, observation, action, reward, next_observation in zip(agent_4001, observation_101, action_101, reward_101, next_observation_101):\n",
    "            agent.learn(observation, action, reward, next_observation,done)\n",
    "        # 更新状态\n",
    "        observation = next_observation\n",
    "        # 上面是Q-learning完整的一步，下面是数据统计\n",
    "        # 统计动作\n",
    "        episode_actions.append(action_101)\n",
    "        # 统计奖励\n",
    "        episode_rewards.append(reward_101)\n",
    "    return episode_rewards, episode_actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = MinorityGame_1()\n",
    "# 创建玩家\n",
    "agent_4001 = [QLearningAgent2(env,gamma=0.9,learning_rate=0.9,epsilon=0.02) for _ in range(4001)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "rewards_0, actions_0 = play_qlearning(env,agent_4001,1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f280e600320>]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([sum(i) for i in actions_0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f280e751e80>]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "env = MinorityGame_1()\n",
    "# 创建玩家\n",
    "agent_4001 = [QLearningAgent2(env,gamma=0.9,learning_rate=0.9,epsilon=0.01) for _ in range(101)]\n",
    "rewards_0, actions_0 = play_qlearning(env,agent_4001,1000)\n",
    "plt.plot([sum(i) for i in rewards_0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Qlearning智能体\n",
    "class QLearningAgent2:\n",
    "    '''\n",
    "    Q-learning智能体实现\n",
    "    '''\n",
    "\n",
    "    def __init__(self, env, gamma=0.9, learning_rate=0.1, epsilon=0.1):\n",
    "        '''\n",
    "        Q-learning智能体初始化:\n",
    "        env 智能体的博弈环境；\n",
    "        gamma 折扣因子，n步后的奖励为 pow(gamma, n)*Rn, gamma越大表示越重视长期收益。\n",
    "        learning_rata 学习率，Qlearning 更新过程为:Q(s,a) += learning_rate * (R + gamma * Qmax - Q(s,a)),\n",
    "                      学习率越大表示越不依赖过去学习的结果\n",
    "        '''\n",
    "        self.gamma = gamma\n",
    "        self.learning_rate = learning_rate\n",
    "        self.epsilon = epsilon\n",
    "        self.action_n = env.action_space.n\n",
    "        self.q = np.zeros((env.observation_space.n, env.action_space.n))\n",
    "        \n",
    "\n",
    "    def decide(self, state):\n",
    "        '''\n",
    "        epsilon-greedy策略，另外Q表所有值相等表示智能体还没有学到任何经验，这时也鼓励探索。\n",
    "        '''\n",
    "        if np.random.uniform() > self.epsilon :\n",
    "            action = self.q[state].argmax()\n",
    "        else:\n",
    "            action = 0 if np.random.randint(self.action_n) < 0.5 else 1\n",
    "        return action\n",
    "    \n",
    "    def learn(self, state, action, reward, next_state, done):\n",
    "        '''\n",
    "        Q(s,a) += learning_rate * (R + gamma * Qmax - Q(s,a)\n",
    "        '''\n",
    "        u = reward + self.gamma * self.q[next_state].max()\n",
    "        td_error = u - self.q[state, action]\n",
    "        self.q[state, action] += self.learning_rate * td_error"
   ]
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